Trained on various human-authored corpora, Large Language Models (LLMs) have demonstrated a certain capability of reflecting specific human-like traits (e.g., personality or values) by prompting, benefiting applications like personalized LLMs and social simulations. However, existing methods suffer from the superficial elicitation problem: LLMs can only be steered to mimic shallow and unstable stylistic patterns, failing to embody the desired traits precisely and consistently across diverse tasks like humans. To address this challenge, we propose IROTE, a novel in-context method for stable and transferable trait elicitation. Drawing on psychological theories suggesting that traits are formed through identity-related reflection, our method automatically generates and optimizes a textual self-reflection within prompts, which comprises self-perceived experience, to stimulate LLMs' trait-driven behavior. The optimization is performed by iteratively maximizing an information-theoretic objective that enhances the connections between LLMs' behavior and the target trait, while reducing noisy redundancy in reflection without any fine-tuning, leading to evocative and compact trait reflection. Extensive experiments across three human trait systems manifest that one single IROTE-generated self-reflection can induce LLMs' stable impersonation of the target trait across diverse downstream tasks beyond simple questionnaire answering, consistently outperforming existing strong baselines.

Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs' capability to act honestly, especially when faced with visually unanswerable questions, remains largely underexplored. This work presents the first systematic assessment of honesty behaviors across various MLLMs. We ground honesty in models' response behaviors to unanswerable visual questions, define four representative types of such questions, and construct MoHoBench, a large-scale MMLM honest benchmark, consisting of 12k+ visual question samples, whose quality is guaranteed by multi-stage filtering and human verification. Using MoHoBench, we benchmarked the honesty of 28 popular MMLMs and conducted a comprehensive analysis. Our findings show that: (1) most models fail to appropriately refuse to answer when necessary, and (2) MMLMs' honesty is not solely a language modeling issue, but is deeply influenced by visual information, necessitating the development of dedicated methods for multimodal honesty alignment. Therefore, we implemented initial alignment methods using supervised and preference learning to improve honesty behavior, providing a foundation for future work on trustworthy MLLMs.

Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on investigating human moderation biases in different scenarios, while moderation biases under power-asymmetric conflicts remain unexplored. Therefore, we aim to investigate the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation (RQ1) and further explore the influence of AI’s suggestions on these biases (RQ2). For this goal, we conducted a mixed design experiment with 50 participants by leveraging the real conflicts between consumers and merchants as a scenario. Results suggest several biases towards supporting the powerful party within these two moderation modes. AI assistance alleviates most biases of human moderation, but also amplifies a few. Based on these results, we propose several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts.

Counterspeech offers a non-repressive approach to moderate hate speech in online communities. Research has examined how counterspeech chatbots restrain hate speakers and support targets, but their impact on bystanders remains unclear. Therefore, we developed a counterspeech strategy framework and built Civilbot for a mixed-method within-subjects study. Bystanders generally viewed Civilbot as credible and normative, though its shallow reasoning limited persuasiveness. Its behavioural effects were subtle: when performing well, it could guide participation or act as a stand-in; when performing poorly, it could discourage bystanders or motivate them to step in. Strategy proved critical: cognitive strategies that appeal to reason, especially when paired with a positive tone, were relatively effective, while mismatch of contexts and strategies could weaken impact. Based on these findings, we offer design insights for mobilizing bystanders and shaping online discourse, highlighting when to intervene and how to do so through reasoning-driven and context-aware strategies.

Cross-language collaborative storytelling plays a vital role in children’s language learning and cultural development, fostering both expressive ability and intercultural awareness. Yet, in practice, children’s participation is often shallow, and facilitating such sessions places heavy cognitive and organizational burdens on coordinators, who must coordinate language support, maintain children’s engagement, and navigate cultural differences. To address these challenges, we conducted a formative study with coordinators to identify their needs and pain points, which guided the design of SparkTales, an intelligent support system for cross-language collaborative storytelling. SparkTales leverages both individual and common characteristics of participating children to provide coordinators with story frameworks, diverse questions, and comprehension-oriented materials, aiming to reduce coordinators’ workload while enhancing children’s interactive engagement. Evaluation results show that SparkTales not only significantly increases coordinators’ efficiency and quality of guidance but also improves children’s participation, providing valuable insights for the design of future intelligent systems supporting cross-language collaboration.

Multimodal content is crucial for click-through rate (CTR) prediction. However, directly incorporating continuous embeddings from pre-trained models into CTR models yields suboptimal results due to misaligned optimization objectives and convergence speed inconsistency during joint training. Discretizing embeddings into semantic IDs before feeding them into CTR models offers a more effective solution, yet existing methods suffer from limited codebook utilization, reconstruction accuracy, and semantic discriminability. We propose RQ-GMM (Residual Quantized Gaussian Mixture Model), which introduces probabilistic modeling to better capture the statistical structure of multimodal embedding spaces. Through Gaussian Mixture Models combined with residual quantization, RQ-GMM achieves superior codebook utilization and reconstruction accuracy. Experiments on public datasets and online A/B tests on a large-scale short-video platform serving hundreds of millions of users demonstrate substantial improvements: RQ-GMM yields a 1.502% gain in Advertiser Value over strong baselines. The method has been fully deployed, serving daily recommendations for hundreds of millions of users.

With the growing ubiquity of recommender systems (RSs), malicious manipulation through shilling attacks, where fake user profiles are injected to alter system outputs, poses increasing threats to system integrity. Existing attack methods often rely on simplified heuristics, require internal RS data, and most overlook user reviews, limiting their stealthiness, realism, and potential impact. Recently, LLM-based user agents are gaining traction in the RS community for their capabilities to simulate human behaviors like rating and review generation. In this context, we propose AgentSA, a low-knowledge shilling attack framework that employs such agents to manipulate recommendations through adversarial yet human-like interactions. We design targeted mechanisms to guide profile construction, memory retrieval, and action generation (including reviews) to maximize manipulation impact while maintaining behavioral camouflage. We evaluate the impact of these agents on various types of RSs and demonstrate that AgentSA consistently outperforms existing low-knowledge attack methods in both effectiveness and stealth. Our findings uncover a concerning new class of threats enabled by LLM-based agents, underscoring the pressing need to bolster RS security against such emerging risks.

As recommender systems become increasingly important components in online communities, studying their impact on these communities becomes ever more crucial. Facing the high costs and ethical risks of real-world social experiments, researchers construct recommendation simulators to study the interactions between recommender systems and users. However, existing simulators face challenges in providing universal, accurate, and scalable interaction modeling for various types of online communities involving millions of contents and users with diverse action types. To address these challenges, we propose RECOSIM, a simulation framework capable of offering efficient recommendation interaction simulations across a wide range of scenarios. RECOSIM decomposes the user agent into five fundamental modules: Encode Model, Decode Model, Activity Model, Scoring Model, and Generation Model, allowing for accurate and extensible modeling of user behavior and interaction dynamics. The recommender system agent adheres to established industry architectures, implementing three stages and four fundamental strategies, thereby improving generalizability across various platforms and the computational efficiency of simulation. Utilizing two real-world datasets (Weibo and Zhihu), we validate the accuracy and stability of each component and the overall framework of RECOSIM, demonstrating the reliability of RECOSIM as a simulation environment. Subsequently, we delve into analyzing the impact of the four fundamental recommendation strategies on online communities, providing design inspirations for enhancing user engagement and community growth.

Online news platforms have become the primary source of information consumption, with recommender systems serving as critical gateways that shape public discourse through their algorithmic power, necessitating rigorous evaluation methodologies. Traditional offline evaluation methods struggle with evolving user behavior and dynamic system adaptation, while online experiments are costly, time-consuming, and ethically challenging. To address these challenges, this paper introduces EvalAgent, a large language model agent system for simulating real-world online news recommender systems. EvalAgent employs Stable Memory (StM) to model users' exploration-exploitation dynamics, mitigating noise from irrelevant interactions by analyzing the distribution density of news articles within the short-term memory, and incrementally maintains the long-term memory to capture users' high-level preferences, thereby enabling a consistent and reliable simulation of sustained interactions. It further incorporates an Environment Interaction Framework (EIF) to enable seamless engagement with real-world recommender systems. This approach yields a precise, scalable, and ethically responsible evaluation framework for news recommender systems. Comprehensive experiments and user studies substantiate EvalAgent's efficacy, with publicly available code to support ongoing research in recommender system evaluation.

Mixture-of-Experts (MoE) is widely adopted to deploy Large Language Models (LLMs) on edge devices with limited memory budgets. Although MoE is, in theory, an inborn memory-friendly architecture requiring only a few activated experts to reside in the memory for inference, current MoE architectures cannot effectively fulfill this advantage and will yield intolerable inference latencies of LLMs on memory-constrained devices. Our investigation pinpoints the essential cause as the remarkable temporal inconsistencies of inter-token expert activations, which generate overly frequent expert swapping demands dominating the latencies. To this end, we propose a novel MoE architecture, Oracle-MoE, to fulfill the real on-device potential of MoE-based LLMs. Oracle-MoE route tokens in a highly compact space suggested by attention scores, termed the oracle space, to effectively maintain the semantic locality across consecutive tokens to reduce expert activation variations, eliminating massive swapping demands. Theoretical analysis proves that Oracle-MoE is bound to provide routing decisions with better semantic locality and, therefore, better expert activation consistencies. Experiments on the pretrained GPT-2 architectures of different sizes (200M, 350M, 790M, and 2B) and downstream tasks demonstrate that without compromising task performance, our Oracle-MoE has achieved state-of-the-art inference speeds across varying memory budgets, revealing its substantial potential for LLM deployments in industry.

Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users’ decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user’s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.

Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.

Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular items. In this paper, we demonstrate that popularity bias arises from the influence of propensity factors during training. Building on this insight, we propose a fair sampling (FS) method that ensures each user and each item has an equal likelihood of being selected as both positive and negative instances, thereby mitigating the influence of propensity factors. The proposed FS method does not require estimating propensity scores, thus avoiding the risk of failing to fully eliminate popularity bias caused by estimation inaccuracies. Comprehensive experiments demonstrate that the proposed FS method achieves state-of-the-art performance in both point-wise and pair-wise recommendation tasks. The code implementation is available at https://github.com/jhliu0807/Fair-Sampling.

LLM-based user agents, which simulate user interaction behavior, are emerging as a promising approach to enhancing recommender systems. In real-world scenarios, users' interactions often exhibit cross-domain characteristics and are influenced by others. However, the memory design in current methods causes user agents to introduce significant irrelevant information during decision-making in cross-domain scenarios and makes them unable to recognize the influence of other users' interactions, such as popularity factors. To tackle this issue, we propose a dual-layer memory architecture combined with a two-step fusion mechanism. This design avoids irrelevant information during decision-making while ensuring effective integration of cross-domain preferences. We also introduce the concepts of interest groups and group-shared memory to better capture the influence of popularity factors on users with similar interests. Comprehensive experiments validate the effectiveness of AgentCF++. Our code is available at https://github.com/jhliu0807/AgentCF-plus.

Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline called CoT-Rec, which integrates two key Chain-of-Thought (CoT) processes-user preference analysis and item perception analysis-into LLM-powered recommendations, thereby enhancing the utilization of LLMs' reasoning abilities. CoT-Rec consists of two stages: (1) personalized information extraction, where user preferences and item perception are extracted, and (2) personalized information utilization, where this information is incorporated into the LLM-powered recommendation process. Experimental results demonstrate that CoT-Rec shows potential for improving LLM-powered recommendations. The implementation is publicly available at https://github.com/jhliu0807/CoT-Rec.

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the ''oracle'' user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn ''forward-looking'' models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin.

Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. Based on the edited profile, DiscomfortFilter facilitates the discomforting recommendations filtering in a plug-and-play manner, maintaining flexibility and transparency. The constructed preference profile improves LLM reasoning and simplifies user alignment, enabling a 3.8B open-source LLM to rival top commercial models in an offline proxy task. A one-week user study with 24 participants demonstrated the effectiveness of DiscomfortFilter, while also highlighting its potential impact on platform recommendation outcomes. We conclude by discussing the ongoing challenges, highlighting its relevance to broader research, assessing stakeholder impact, and outlining future research directions.

Abstract Video-sharing platforms such as YouTube are popular today, not only for entertainment but also for learning, e.g., for understanding lecture content or acquiring everyday skills. Some platforms, like Bilibili, a popular video-sharing platform in China, even introduced note-taking and sharing features to further foster such learning practices. While note-taking and sharing have been extensively explored in environments specifically for learning, their use around these social media platforms such as YouTube and Bilibili remains under-studied. In this paper, we present a qualitative study with 15 participants who have used the note-taking and sharing feature called BNote launched on Bilibili. Our study reveals how note-taking and sharing are used as a way to cultivate a space for learning on such a general video-sharing platform by structuring, supplementing, and substituting user-generated videos and by leveraging social forces for motivation, self-discipline, and organization for learning. We end by discussing our findings and providing design recommendations to better support a learning space on social media platforms.

Older adults (OAs) usually face various challenges when using smartphones due to their limited knowledge and the declines in memory and information processing capabilities. Many studies in HCI and CSCW communities have focused on supporting OAs to independently use smartphones. However, compared to independent exploration, support from younger family members (YFMs) has specific advantages in problem understanding, solution personalization, and security protection. However, OAs and YFMs generally have gaps in time, knowledge, and experience, affecting the efficiency of support and their experience. For this problem, we conduct a formative study to gather insights into OAs and YFMs' perspectives and expectations in the supporting procedure. Then we introduce chatbot to mediate the gaps between OAs and YFMs and build a system named WePilot to assist them to collaboratively solve smartphone usage problems. Evaluations with 12 pairs of participants (OA and corresponding YFM) suggest WePilot's strengths in improving problem solving efficiency and OAs and YFMs' experience. Based on these findings, we propose several insights into the future design of intergenerational technical support systems.

Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process, wherein detection is just an initial task and a user can have further needs such as rationale understanding and content modification. For this problem, we conduct a need-finding study to investigate people's diverse needs in toxicity censorship and then build a ChatGPT-based censorship tool named DeMod accordingly. DeMod is equipped with the features of explainable De tection and personalized Mod ification, providing fine-grained detection results, detailed explanations, and personalized modification suggestions. We also implemented the tool and recruited 35 Weibo users for evaluation. The results suggest DeMod's multiple strengths like the richness of functionality, the accuracy of censorship, and ease of use. Based on the findings, we further propose several insights into the design of content censorship systems.

Denoising-based diffusion models have attained impressive image synthesis; however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with inter-frame motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found athttps://drmo-denoising-reuse.github.io.

Performing user modeling on two or more social media platforms collaboratively and complementing each other (cross-site user modeling) has been a significant problem in the area of social media mining in recent years. The core of this problem is to get to know a person’s identities on multiple platforms and then train user models collaboratively among these platforms. However, for privacy protection, many people do not want their identities on different platforms to be linked and disclosed. For this problem, we set cross-site Content Preference Prediction as a task and propose a cross-site user modeling method without cross-site User Identity Linkage (UIL). The core thought borrowed from privacy-preserving recommender system research is to organize social media identities into groups to hide the identity linkage among platforms. Experiments on real-world datasets suggest that our method outperforms the existing cross-site user modeling methods with cross-site UIL regarding several metrics.

Danmu (or “bullet screen”), a popular feature on video sharing platforms, plays a crucial role in facilitating knowledge sharing and learning. In recent years, danmu has drawn attention to automatic generation methods. However, existing methods mostly utilize limited content sources, such as the video itself (e.g., subtitles) and neighboring danmus, while other valuable sources remain underexplored. To this end, this article proposes a Category-Guided Note-to-Danmu conversion model (CG-NTD) by leveraging user-generated notes. The model is designed to identify unique contents within the notes and convert them into danmus while also showing the source note categories. CG-NTD classifies the notes by fusing them with subtitle and neighboring danmu features. Then, it uses a cross-attention mechanism to integrate the note’s category feature with note, subtitle, and danmu contexts to identify three keywords from the notes as the generated danmus. Using Bilibili as the research site, we implement a plugin prototype named BNoteToDanmu. Automatic and human evaluations reveal that CG-NTD outperforms BiLSTM, mT5, and BERT baselines in Precision, Recall, and F1-score metrics and generates more understandable and relevant danmus than ChatGPT. Moreover, the plugin demonstrates promising applications, such as assisting users in viewing videos, posting danmus, and recognizing high-quality notes. These findings offer insights into leveraging user creations to generate danmu to enhance its learning value on video sharing platforms.

Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical values utilizing Moral Foundation Theory. Moving beyond conventional discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs’ value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that substantially enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, offering a promising initial step in studying the ethical values of LLMs.

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and long-tail distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, outlier clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

Temporal recommendation methods can achieve superior accuracy due to updating user/item embeddings continuously once obtaining new interactions. However, the randomness of user behaviors will introduce noises into the user interactions and cause the deviation in the modeling of user preference, resulting in sub-optimal performance. To this end, we propose NeuFilter, a robust temporal recommendation algorithm based on neural Kalman Filtering, to learn more accurate user and item embeddings with noisy interactions. Classic Kalman Filtering is time-consuming when applied to recommendation due to its covariance matrices. Thus, we propose a neural network solution to Kalman Filtering, so as to realize higher efficiency and stronger expressivity. Specifically, NeuFilter consists of three alternating units: 1) prediction unit, which predicts user and item embeddings based on their historical embeddings; 2) estimation unit, which updates user and item embeddings in a manner similar to Kalman Filtering; 3) correction unit, which corrects the updated user and item embeddings from estimation unit to ensure reliable estimation and accurate update. Experiments on two recommendation tasks show that NeuFilter can achieve higher accuracy compared with the state-of-the-art methods, while achieving high robustness. Moreover, our empirical studies on a node classification task further confirm the importance of handling noises in tasks on temporal graph, shedding a new light on temporal graph modeling.

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users, so that the recognized interaction patterns are not fully user-matched and easily impacted by other users with different interaction behaviors, resulting in sub-optimal recommendation performance. To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. By linearly combining these two modules, HiGSP can recognize user-matched interaction patterns, so as to model user preference and predict user future interactions more accurately. Extensive experiments on six real-world datasets demonstrate the superiority of HiGSP compared to other GCN-based and GSP-based recommendation methods in terms of efficacy and efficiency.

Influence maximization (IM) has been extensively researched in the information propagation field and applied in various domains. However, existing studies on the IM have primarily focused on network structure, and lack the in-depth exploration of online network complexities, like personal history or preference. In this paper, a heterogeneous cross-comparison improved Kshell algorithm (HCCKshell) is proposed to solve IM, applying users’ multi-dimensional attributes in the propagation, including social history and topological structure . Specifically, the model learns users’ potential representation of historical content preferences and topological structure based on the Encoder and GCN thoughts, then defines the heterogeneous similarity and the heterogeneous information entropy to measure users’ influence ability and provide reliability assurance on the propagation. To improve the performance, a cross-comparison improved K-shell heuristic algorithm based on the heterogeneous information entropy is proposed to find a valid influential seed set. Furthermore, the experiments on multiple real large-scale datasets and their results indicate that our HCCKshell algorithm is more effective than baseline algorithms on both effect and performance.

Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a jargon classifier, similar word identification based on a jargon synonyms selector, and representative text selection based on an example sentence selector. A jargon interpreter was also implemented to demonstrate the usage and usefulness of the interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.

The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human–computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems’ responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.

Studying the evolution of online news communities is essential for improving the effectiveness of news recommender systems. Traditionally, this has been done through empirical research based on static data analysis. While this approach has yielded valuable insights for optimizing recommender system designs, it is limited by the lack of appropriate datasets and open platforms for controlled social experiments. This gap in the existing literature hinders a comprehensive understanding of the impact of recommender systems on the evolutionary process and its underlying mechanisms. As a result, suboptimal system designs may be developed that could negatively affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which sheds new light on the design of recommender systems.

Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video and aiming to consume the video itself. In this article, we design and implement BNoteHelper, a note-based video outline prototype that generates outline titles by extracting user-generated notes on Bilibili, using the BART model fine-tuned on a built dataset. As a browser plugin, BNoteHelper provides users with video overview and navigation as well as note-taking template, via two main features: outline table and navigation marker. The model and prototype are evaluated through automatic and human evaluations. The automatic evaluation reveals that, both before and after fine-tuning, the BART model outperforms T5-Pegasus in BLEU and Perplexity metrics. Also, the results from user feedback reveal that the generation outline sourced from notes is preferred by users over that sourced from video captions due to its more concise, clear, and accurate characteristics but also too general with less details and diversities sometimes. Two features of the video outline are also found to have respective advantages, especially in holistic and fine-grained aspects. Based on these results, we propose insights into designing a video summary from the user-generated creation perspective, customizing it based on video types, and strengthening the advantages of its different visual styles on video-sharing platforms.

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.

Cross-site user identity linkage (UIL) aims to link the identities of the same person across different social media platforms. Social media practitioners and service providers can construct composite user portraits based on cross-site UIL, which helps understand user behavior holistically and conduct accurate recommendations and personalization. However, many social media users expect each profile to stay within the platform where it was created and thus do not want the identities of different platforms to be linked. For this problem, we first investigate the approaches people would like to use to defend against cross-site UIL and the corresponding challenges. Based on the findings, we build an adversarial framework, DeLink, based on the thoughts of adversarial text generation to help people improve their social media screen names to defend against cross-site UIL. DeLink can support both Chinese and English languages and has good generalizability to the varying numbers of social media accounts and different cross-site user identity linkage models. Extensive evaluations validate DeLink’s better performance, including a higher success rate, higher efficiency, less impact on human perception, and capability to defend against different cross-site UIL models.

Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.

Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising.It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.However, existing methods face three significant challenges.First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases.Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions.Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer.This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges.As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order.Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance.Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN.Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters.The code is available on https://github.com/anonctr/GDCN.

Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained, modular-level learning dynamics of over-parameterized models to attain a more efficient and fruitful training strategy. Empirical evidence reveals that when scaling down into network modules, such as heads in self-attention models, we can observe varying learning patterns implicitly associated with each module's trainability. To describe such modular-level learning capabilities, we introduce a novel concept dubbed modular neural tangent kernel (mNTK), and we demonstrate that the quality of a module's learning is tightly associated with its mNTK's principal eigenvalue $\lambda_{\max}$. A large $\lambda_{\max}$ indicates that the module learns features with better convergence, while those miniature ones may impact generalization negatively. Inspired by the discovery, we propose a novel training strategy termed Modular Adaptive Training (MAT) to update those modules with their $\lambda_{\max}$ exceeding a dynamic threshold selectively, concentrating the model on learning common features and ignoring those inconsistent ones. Unlike most existing training schemes with a complete BP cycle across all network modules, MAT can significantly save computations by its partially-updating strategy and can further improve performance. Experiments show that MAT nearly halves the computational cost of model training and outperforms the accuracy of baselines.

In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information, resulting in suboptimal performance. To this end, we propose øurs, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. Specifically, øurs consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value ecomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs. Then, we fuse the predictions from the triple structural information sources to obtain the final recommendation results. By analyzing the relationship between the SVD-based and the recently emerging graph signal processing (GSP)-based collaborative filtering methods, we find that the essence of SVD is an ideal low-pass graph filter, so that the interest vector space in øurs can be extended to achieve explainable and interactive recommendation, making it possible for users to actively break through the information cocoons. Experiments on six public datasets demonstrated the effectiveness of øurs in accuracy, explainability and interactivity.

Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.

The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art CF methods and, as a nonparametric method, PGSP has very high training efficiency.

Nowadays, using the online whiteboard to share knowledge in distributed meetings has become a common practice. Existing studies and practices have attempted to visualize attendees’ interactive activities in whiteboard tools to support the virtual team’s workspace awareness (WA). However, the impact of such visual cues on meeting success remains unclear. For this purpose, we primarily explore whether and to what extent WA cues are conducive to meeting outcome. This study applies activity theory to guide our prototype design and research analysis. A customized web-based whiteboard interface is implemented under two conditions. We conduct a study with 42 subjects in a distributed meeting scenario via a controlled experiment. Also, we analyze the system affordance via user experience. The results demonstrate that the benefits of WA cues to meeting outcome are especially embodied in goal attainment and quality of contributions, but not effectively supported in productivity and user satisfaction. Moreover, subjects report that they do not feel distracted by the system’s visual cues because they do not notice those cues most of the time and use them only when needed. Drawing upon findings from our trial work, we provide several implications for designing a collaborative knowledge-sharing environment to assist the visual support of WA in distributed meetings.

Online question and answer (Q&A) communities have become an important platform for sharing and gaining knowledge. To ensure the quality of posts, Q&A sites support and encourage collaborative editing. However, some undesired, inaccurate, or poor quality edits will be rejected by rollbacks. Most Q&A communities identify rollback edits by human review, which is not efficient and timely enough. To address this problem, we aim to construct a model that can automatically predict whether an edit should be rolled back. Considering that in addition to changing the content of a post, editing activities usually generate metadata information. Therefore, we first conduct an empirical study to investigate the metadata factors that may be associated with edit rollbacks. The analysis results indicate that metadata features from multiple dimensions have different levels of significant effects on edit rollback, and edit textual contents are dependent on metadata. Based on these results, we propose a deep learning model, namely DFMT (Deep Fusion of Metadata and Text), for rollback prediction. Experimental results on a real Stack Overflow dataset demonstrates the effectiveness of DFMT compared to several baselines.

The barrier-free map is a new type of map, the demand for which is increasing in modern society, and it is necessary to rely on crowdsourcing platforms to complete the labeling work of barrier-free facilities. Nowadays more people with disabilities are participating in crowdsourcing work. They tend to work with familiar people and hope to maximize their value. However, there is very little research about automatic management of organizations and tasks for the disabled to assist them to complete the work on the crowdsourcing platform. In this paper, we design a new team formation method based on the theory of socially embedded work of the disabled (TFSEW) and a bi-objective task recommendation method (BOTR), in order to ensure the efficiency of task completion and promote the participation of the disabled, thereby realizing their value in the work environment. Our method proves to be superior through experiments. At the same time, through questionnaire, people with disabilities show great anticipation for participating in the tasks in form of team and receive tasks recommended by the system.

Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider these two factors together, which may lead to noisy user modelling when the two factors diverge. In addition, they usually require time-consuming parameter learning with back-propagation, which is prohibitive for real-time user preference modelling. To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. Firstly, to take advantage of the collaborative relationships, we propose an incremental graph embedding engine to obtain user/item embeddings, which is an Online-Monitor-Offline architecture consisting of an Online module to approximately embed users/items over time, a Monitor module to estimate the approximation error in real time and an Offline module to calibrate the user/item embeddings when the online approximation errors exceed a threshold. Meanwhile, we integrate attribute information into the model, which enables FreeGEM to better model users belonging to some under represented groups. Secondly, we design a personalized dynamic interaction pattern modeller, which combines dynamic time decay with attention mechanism to model user short-term interests. Experimental results on two link prediction tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy while achieving over 36X improvement in efficiency. All code and datasets can be found in https://github.com/FudanCISL/FreeGEM.

CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance. Recently, several methods tried to learn vector-level weights for feature representations to address the fixed representation issue. However, they only produce linear transformations to refine the fixed feature representations, which are still not flexible enough to capture the varying importance of each feature under different contexts. In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. FRNet consists of two key components: 1) Information Extraction Unit (IEU), which captures contextual information and cross-feature relationships to guide context-aware feature refinement; and 2) Complementary Selection Gate (CSGate), which adaptively integrates the original and complementary feature representations learned in IEU with bit-level weights. Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in many existing methods to boost their performance. Comprehensive experiments are conducted to verify the effectiveness, efficiency, and compatibility of FRNet.

To meet different social purposes, users usually share content related to the same topic or event to multiple social media platforms (cross-platform content sharing). As the differences of social norms and audiences among these social ecosystems, there are differences in the use of words and expressions in different platforms, resulting in different language styles among different platforms. In reality, it is usually difficult for users to grasp the consistency between the language style of posts to be published and that of a platform as the problem of context collapse. To address this problem, firstly, we conduct an study to investigate users' content sharing practices across two Chinese popular social media platforms (Douban and Weibo). The results indicate that: 1) there are significant linguistic differences between different platforms; 2) users' content sharing practices are personalized, and the style of their newly shared content is correlated with their historical posts. Secondly, based on the above findings, we propose a personalized cross-platform post style transfer model. The model can automatically transfer users' posts from one platform's language style to the target platform's language style, while preserving the content and reflecting users' personalized characteristics as much as possible. Experiments on the datasets collected from Douban and Weibo show that our model generally outperforms other comparison models on both style transfer and personalization metrics.

Cross-modal learning is essential to enable accurate fake news detection due to the fast-growing multimodal contents in online social communities. A fundamental challenge of multimodal fake news detection lies in the inherent ambiguity across different content modalities, i.e., decisions made from unimodalities may disagree with each other, which may lead to inferior multimodal fake news detection. To address this issue, we formulate the cross-modal ambiguity learning problem from an information-theoretic perspective and propose CAFE — an ambiguity-aware multimodal fake news detection method. CAFE consists of 1) a cross-modal alignment module to transform the heterogeneous unimodality features into a shared semantic space, 2) a cross-modal ambiguity learning module to estimate the ambiguity between different modalities, and 3) a cross-modal fusion module to capture the cross-modal correlations. CAFE improves fake news detection accuracy by judiciously and adaptively aggregating unimodal features and cross-modal correlations, i.e., relying on unimodal features when cross-modal ambiguity is weak and referring to cross-modal correlations when cross-modal ambiguity is strong. Experimental studies on two widely used datasets (Twitter and Weibo) demonstrate that CAFE outperforms state-of-the-art fake news detection methods by 2.2-18.9% and 1.7-11.4% on accuracy, respectively.

Auxiliary information, such as reviews, have been widely adopted to improve collaborative filtering (CF) algorithms, e.g., to boost the accuracy and provide explanations. However, most of the existing methods cannot distinguish between co-appearance and causality when learning from the reviews, so that they may rely on spurious correlations rather than causal relations in the recommendation — leading to poor generalization performance and unconvincing explanations. In this paper, we propose a Recommendation via Review Rationalization (R3) method including 1) a rationale generator to extract rationales from reviews to alleviate the effects of spurious correlations; 2) a rationale predictor to predict user ratings on items only from generated rationales; and 3) a correlation predictor upon both rationales and correlational features to ensure conditional independence between spurious correlations and rating predictions given causal rationales. Extensive experiments on real-world datasets show that the proposed method can achieve better generalization performance than state-of-the-art CF methods and provide causal-aware explanations even when the test data distribution changes.

Recommender systems are incremental in nature. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users/items from temporal interaction graphs, so that their user/item embeddings can evolve together with the graph structures. However, these methods are faced with two key challenges: 1) model training and/or updating are time-consuming and 2) new users/items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. FIRE is non-parametric which does not suffer from the time-consuming back-propagations as in previous learning-based methods, significantly improving the efficiency of model updating. In addition, we encode user/item temporal information and side information by designing new graph filters in FIRE, which can capture the temporal dynamics of users/items and address the cold-start issue for new users/items, respectively. Experimental studies on four popular datasets demonstrate that FIRE can improve the accuracy by a large margin and improve the model updating efficiency by at least 3X compared with the state-of-the-art incremental recommendation algorithms. The Code is available at https://github.com/Yaveng/FIRE.

Abstract The development of Information and Communication Technologies (ICT) and Web 2.0 promotes the emergence of diverse social ecosystems like social Internet of Things (IoT), social media and online communities. User-generated textual content (UGTC), which consists of unstructured texts, is the most important and common type of user-generated content in social ecosystems. UGTC in social ecosystems is generated according to two types of context information—global context (topics) and local context (semantic regularities). For UGTC modeling, topic models just consider global context but ignore semantic regularities, while semantic embedding models are on the opposite. So only utilizing topic models or semantic embedding models to model UGTC suffers from some drawbacks. For this problem, we propose a semantic embedding enhanced topic model named SEE-Twitter-LDA for accurately modeling UGTC in social ecosystems. The core of SEE-Twitter-LDA is that words are generated according to mutual semantic information of topics and semantic regularities. So global context and local context are jointly considered for UGTC modeling. By utilizing 553 098 tweets sampled from Twitter and 211 233 posts sampled from Weibo, we validate SEE-Twitter-LDA’s better performance on perplexity, topic divergence and topic coherence versus existing related models.

Social media users often suffer from the problem of content over-disclosure. Most existing studies attempt to solve this problem by recommending proper audiences for users when sharing content. However, the audience management strategy cannot filter out sensitive information from the post and narrow the scope of content permeation. On the contrary, this paper conducts research from the content perspective and aims to design a content censorship model to help users evaluate the publicity of a post and find the sensitive information from it. The user can revise the content accordingly to achieve goals of sensitive information protection and broader content permeation. For this intention, we first built a dataset to explore the factors related to the public level of a post and the sensitive information. Based on the findings, a novel personalized multi-task content censorship model was built using several state-of-the-art deep learning techniques such as Seq2Seq and Co-training. We also implemented a prototype, i.e. a Browser plugin-based content censorship tool, by utilizing Weibo as a research site. Our model and its prototype were evaluated through automatic and human evaluations. The automatic evaluation suggests that our model outperforms the baseline methods on several metrics including precision, recall, and F1-score. The human evaluation also reveals that our model and prototype play an important role in helping users identify sensitive information. Based on these results, we proposed several insights for the future design of the social media content censorship system.

Mobility is the key for people with disabilities to have full participation in life. To support their mobility, previous work primarily focused on accessibility as an attribute of the external environment to be evaluated, labeled, visualized, and improved. Relatively less work has looked at how people with disabilities go out and move around in practice and work to achieve mobility. This paper presents a qualitative study of 14 wheelchair users' travel practices in everyday life in China, highlighting urban accessibility as alignment work -- the use of an awareness of the conditions along the journey to continuously align and re-align various resources to ensure accessibility and mobility across contexts and moments. By highlighting alignment work, we present another side of the story of urban accessibility, and broaden design considerations to support mobility for wheelchair users.

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multi-task video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset.

User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.

Graph neural networks (GNN) recently achieved huge success in collaborative filtering (CF) due to the useful graph structure information. However, users will continuously interact with items, which causes the user-item interaction graphs to change over time and well-trained GNN models to be out-of-date soon. Naive solutions such as periodic retraining lose important temporal information and are computationally expensive. Recent works that leverage recurrent neural networks to keep GNN up-to-date may suffer from the "catastrophic forgetting'' issue, and experience a cold start with new users and items. To this end, we propose the incremental graph convolutional network (IGCN) --- a pure graph convolutional network (GCN) based method to update GNN models when new user-item interactions are available. IGCN consists of two main components: 1) a historical feature generation layer, which generates the initial user/item embedding via model agnostic meta-learning and ensures good initial states and fast model adaptation; 2) a temporal feature learning layer, which first aggregates the features from local neighborhood to update the embedding of each user/item within each subgraph via graph convolutional network and then fuses the user/item embeddings from last subgraph and current subgraph via incremental temporal convolutional network. Experimental studies on real-world datasets show that IGCN can outperform state-of-the-art CF algorithms in sequential recommendation tasks.

People with disabilities face a lot of inconvenience in daily life, even in their homes where are the most familiar environment. They are difficult to complete the operation of simple household equipment, and they rely heavily on others. In order to solve the above problems, we presented an operation behavior model for people with disabilities, analyzed their operation behavior and proposed a prediction method for them in smart home. This method can effectively reduce the dependence of disabled people on others and improve their self-reliance. Different from the existing operation behavior prediction methods in smart home, our method specialized in the operation behavior characteristics of the people with disabilities. In the process of modeling, some special elements of disabled groups are added in, such as types of disabilities and interaction modes. At the same time, the collaborative operation between users and collaborative operation relationship between various devices are also taken into our model. The experimental results shown that our method proposed in this paper has higher accuracy.

The correctness of the procuratorial case handling has a significant impact on social fairness and justice, which makes it essential to review and supervise procuratorial cases. However, these cases often involve a variety of types of data items and activities and complex processing logic, which makes it difficult to check the source of the data and verify the compliance of the process. To solve this problem, we propose a provenance solution for the procuratorial case processing scenario in this paper. We first sort out four types of questions that need to be answered in the case of the procuratorate. Then we propose two kinds of provenance to give these explanations through a single instance or multiple instances. Besides, we choose graph database Neo4j as a storage plan and provide corresponding query plans for the provenance, then evaluate their performance with our prototype system and the data from procuratorial test cases. The result shows that the query efficiency of our solution will not fluctuate greatly due to the depth of the query or the amount of data, which is suitable for the work scene of the procuratorate.

Multidisciplinary provenance helps to record the detailed processes about data derivation during multidisciplinary collaboration, which is vital to result reproducibility, trustworthiness reinforcement and other further usage. However, too detailed multidisciplinary provenance information often overwhelms user due to provenance bushiness, provenance deepness and semantic complexity. This paper presents a summarization approach to simplify provenance information based on homogeneity and semantic distance. We first specify a collaborative provenance model to records multidisciplinary collaboration processes. By analyzing the difficulties of understanding multidisciplinary provenance information, a multi-granular provenance summarization approach is given. Finally, discipline-specified provenance views are provided to meet different concerns. Experimental analysis shows the effectiveness our approach from the perspectives of conciseness.

Gaze-on-screen tracking, an appearance-based eye-tracking task, has drawn significant interest in recent years. While learning-based high-precision eye-tracking methods have been designed in the past, the complex pre-training and high computation in neural network-based deep models restrict their applicability in mobile devices. Moreover, as the display frame rate of mobile devices has steadily increased to 120 fps, high-frame-rate eye tracking becomes increasingly challenging. In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. Specifically, GazeHFR classifies the eye movement into two distinct phases, i.e., saccade and smooth pursuit, and leverages inter-frame motion information combined with lightweight learning models tailored to each movement phase to deliver high-efficient eye tracking without affecting accuracy. Compared to prior art, Gaze-HFR achieves approximately 7x speedup and 15% accuracy improvement on mobile devices.

Online videos are playing an increasingly important role in timely information dissemination especially during public crises. Video commentary, synchronous or asynchronous, is indispensable in viewers' engagement and participation, and may in turn contribute to video with additional information and emotions. Yet, the roles of video commentary in crisis communications are largely unexplored, which we believe that an investigation not only provides timely feedback but also offers concrete guidelines for better information dissemination. In this work, we study two distinct commentary features of online videos: traditional asynchronous comments and emerging synchronous danmaku. We investigate how users utilize these two features to express their emotions and share information during a public health crisis. Through qualitative analysis and applying machine learning techniques on a large-scale danmaku and comment dataset of Chinese COVID-19-related videos, we uncover the distinctive roles of danmaku and comments in crisis communication, and propose comprehensive taxonomies for information themes and emotion categories of commentary. We also discover the unique patterns of crisis communications presented by danmaku, such as collective emotional resonance and style-based highlighting for emphasizing critical information. Our study captures the unique values and salient features of the emerging commentary interfaces, in particular danmaku, in the context of crisis videos, and further provides several design implications to enable more effective communications through online videos to engage and empower users during crises.

Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks.

Many popular social network sites (SNSs) provide the post-syncing functionality, which allows users to synchronize posts automatically among different SNSs. Nowadays there exists divergence on this functionality from the view of sink SNS. The key to solving this problem is to understand the characteristics of users’ post-syncing practice and goals and evaluate whether they are consistent with an SNS’s norms, cultures, and goals. However, studying and understanding the characteristics of post-syncing practice and goal are challenging tasks as a result of the difficulty of data sampling and the complexity of post-syncing behavior. In this article, we focus on investigating this question by quantitative analysis in combination with qualitative analysis. In the quantitative study, by utilizing 211,233 synced-posts sampled from Weibo, we aim to investigate characteristics of post-syncing from three perspectives: user, content, and goal. The results suggest that post-syncing plays an important role in exhibiting one’s current activities, creations, and skills as well as advertisements but involves a risk of exhibiting personal sensitive profiles. To understand the results, we present an interview-based qualitative study based on thematic analysis. It indicates that the publicity, urgency, and remarkableness of contents and differences of social affordances and social circles between sink SNS and source SNS as well as the one-time consent of post-syncing authentication jointly account for the major role of post-syncing. Based on these results, we propose insights for post-syncing functionality’s adoption, design, and promotion.

Abstract Question‐Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high‐quality content continuously contributed by users with high‐level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining well‐designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian‐Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real‐world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state‐of‐the‐art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian–Gamma mixture clustering model is capable of detecting excellent domain problem‐solving experts who exhibit both domain interest and expertise.

People tend to utilize multiple social network sites (SNSs) simultaneously to maintain some social relationships, which results that there are many overlapping relationships and interactions among SNSs. Although many studies have focused on social interaction and cross-SNS user footprint analysis and understanding, little research investigates social interaction from a perspective of two or more SNSs, and the interplay of interaction among SNSs has been unknown. In this paper, we aim to explore whether interaction building in a new SNS hinders the interaction frequency in an existing site, and if so, what kinds of users and relationships’ interactions are more or less likely to be affected. For these questions, we sampled 7,015 pairs of overlapping identities, 23,590 pairs of overlapping relationships and 6,771 pairs of overlapping interactions from Weibo and Douban and made analysis by combining multiple methods like Regression Discontinuity Design and Random-effects Negative Binomial Regression model. Our results suggest that no matter from the perspective of individuals or from the perspective of relationships, interaction construction in a new SNS is detrimental to interaction frequency in an existing site. Based on our findings, we also propose several valuable insights about how to enhance social interaction and promote its retention when users are involved into interacting practice in multiple platforms.

Toxicity in online discussions has been an intriguing phenomenon and an important problem. In this paper, we seek to better understand toxicity dynamics in online discussions via a case study on Reddit that explores the antecedents and consequences of toxicity in text. We inspected two dimensions of toxicity: language toxicity, i.e. how toxic the text itself is; and toxicity elicitation, i.e. how much toxicity it elicits in its response. Through regression analyses on Reddit comments, we found that both author propensity and toxicity in discussion context were strong positive antecedents of language toxicity; meanwhile, language toxicity significantly increased the volume and user evaluation of the discussion in some sub-communities, while toxicity elicitation showed mixed effects. We then discuss how our results help understand and regulate toxicity in online discussions by interpreting the complicated triggers and outcomes of toxicity.

Matrix approximation (MA) techniques, which can address the data sparsity issue by reducing the dimensionalities of user/item feature vectors, have been extensively adopted in today's recommender systems. However, many existing MA methods, e.g., singular value decomposition (SVD), cannot acquire sparse user or item feature matrices using the popular stochastic gradient descent (SGD)-based training method, which leads to the suboptimal accuracy. In this paper, we propose a matrix approximation method with cumulative penalty, which can improve the classic SGD-based training process by keeping track of the cumulative penalty and applying it to the feature matrix. After applying the penalty to the feature matrices, we can rapidly make many user/item features be zero during the training process and thus obtain a sparse user/item feature matrix to improve the recommendation accuracy. In addition, a parallel training method is adopted in the proposed method, which can ensure faster convergence. Our empirical studies on the MovieLens dataset show that the proposed method can significantly outperform the regularized SVD method in terms of accuracy and scalability.

User ratings on items are noisy in real-world recommender systems, which raises challenges to matrix approximation (MA)-based collaborative filtering (CF) algorithms — the learned models will be easily biased to the noisy training data and yield low generalization performance. This paper proposes a noise-resilient matrix approximation (NORMA) method, which can achieve less biased matrix approximation and thus more accurate collaborative filtering. In NORMA, an adaptive weighting strategy is proposed to decrease the gradient updates of noisy ratings, so that the learned MA models will be less prone to the noisy ratings. Theoretical analyses show that NORMA can achieve better generalization performance than standard matrix approximation methods. Experimental studies on real-world datasets demonstrate that NORMA can outperform state-of-the-art matrix approximation-based collaborative filtering methods in recommendation accuracy.

Online question and answer (Q&A) sites are platforms allowing users to seek information or social support, which is sometimes socially uncomfortable. A major challenge in Q&A sites is how to encourage user contribution. Theory of linguistic politeness informs us that politeness is able to ease the tension resulting from face-threatening acts (FTA), and it is applicable to Q&A sites. However, Stack Overflow (SO), one of the most popular Q&A site for programmers, denies the use of linguistic politeness (e.g., greetings, salutations, and gratitude expressions), aiming at building a nonsocial platform. This paper focuses on a specific politeness strategy-expressing gratitude-in Q&A sites, and examines its effect on answerers' future contributions from the aspects of quality. Results indicate that gratitude expressions in comments can motivate answerers to generate higher quality content.

Gradient-based learning methods such as stochastic gradient descent are widely used in matrix approximation-based collaborative filtering algorithms to train recommendation models based on observed user-item ratings. One major difficulty in existing gradient-based learning methods is determining proper learning rates, since model convergence would be inaccurate or very slow if the learning rate is too large or too small, respectively. This paper proposes AdaError, an adaptive learning rate method for matrix approximation-based collaborative filtering. AdaError eliminates the need of manually tuning the learning rates by adaptively adjusting the learning rates based on the noisiness level of user-item ratings, using smaller learning rates for noisy ratings so as to reduce their impact on the learned models. Our theoretical and empirical analysis shows that AdaError can improve the generalization performance of the learned models. Experimental studies on the MovieLens and Netflix datasets also demonstrate that AdaError outperforms state-of-the-art adaptive learning rate methods in matrix approximation-based collaborative filtering. Furthermore, by applying AdaError to the standard matrix approximation method, we can achieve statistically significant improvements over state-of-the-art collaborative filtering methods in both rating prediction accuracy and top-N recommendation accuracy.

Credit scoring plays an important role in financial institutions and debt based crowdfunding platforms as well as peer to peer lending platforms. In the last few years, adopting ensemble methods for credit scoring has become much more popular. However, the performance of ensemble methods is easily affected by the parameter settings and the number of base classifiers. Ensemble classification based on clustering is able to determine the best number of base classifiers automatically by clustering and find optimal parameter settings for base classifiers by training them individually on the training subsets combined by clusters. By this way, the adverse effect of manually setting the parameters and the number of base classifiers can be avoided. However, the different contributions of attributes to the distance metrics are not considered in conventional clustering methods, which may decrease the performance of ensemble classifiers based on them. Moreover, unbalanced training subsets decrease the performance of base classifiers, which results in the bad performance of ensemble classifiers. In our approach, to address the above problems, we first assign different weights to different variables when measuring the distance between two instances in the clustering step, and then adopt Subagging resampling method to deal with unbalanced training subsets in the training process. Experimental results show that our approach can improve the performance of the ensemble classifier.

Collaborative Filtering (CF) is widely applied to personalized recommendation systems. Traditional collaborative filtering techniques make predictions through a user-item matrix of ratings which explicitly presents user preference. With the increasingly growing number of users and items, insufficient rating data still leads to the decreasing predictive accuracy with traditional collaborative filtering approaches. In the real world, however, many different types of user feedback, e.g. review, like or not, votes etc., co-exist in many online content providers. In this paper we integrate rating data with some other new types of user feedback and propose a multi-task matrix factorization model in order for flexibly using multiple data. We use a common user feature space shared across sub-models in this model and thus the model can simultaneously train the corresponding sub-models with every training sample. Our experiments indicate that new types of user feedback really work and show improvements on predictive accuracy compared to state-of-the-art algorithms.

Nowadays, with the rapid development of technology in pervasive healthcare management field, health data shows an explosive growth, with the characters of collaborative, heterogeneous and multi-source, which increase the complexity and difficulty of data assessment. It is critical to ensure health data quality so that effective decisions can be made. In this paper, we propose a method of electronic health data quality assessment based on data provenance. We first conduct a qualitative analysis to explore the current health data condition. Then, we introduce an instancing provenance model for health data analysis. Based on this model, we propose a data assessment framework for health data and use a prototype system to verify the effectiveness of our method. It shows that by enabling data provenance, we significantly reduce the input error and improve the quality of data processing.

In the past decade, online news consumption has been steadily growing. New articles are published every few minutes, and user preferences are also constantly changing. Traditional recommender systems update model at regular intervals, which cannot adjust recommendation list dynamically according to the changes of user preferences. In this paper, we propose a hybrid recommendation model which contains two key components: incremental update item-based collaborative filtering (CF) and latent semantic analysis based relative term frequency algorithms. The hybrid recommendation model adjusts recommendation list dynamically by updating similarity table of items incrementally in incremental update item-based CF module, moreover, combining collaborative filtering and content-based algorithm ensures the relevance of recommendation articles. Results show that our proposed hybrid recommendation model outperforms traditional recommender approaches.

The address space transformation (AST) algorithm is proposed to solve the classical consistency maintenance problem in real-time collaborative editing systems, which is widely adopted in web 2.0, mobile comment and itinerary planning applications. None of the state-of-art AST approaches, however, support string-wise operations natively. They normally split a string into individual characters, thus eliciting low efficiency when user-based operations such as copy and paste are executed. In this paper, we propose a string-wise address space transformation algorithm called ASTS which mainly aims to address two challenges as follows: (1) How to construct a simple model for the string-wise operation. (2) How to maintain stable and high response algorithm performance. Two experiments are given to verify the adopted algorithm's effectiveness in diverse perspective. Compared with traditional AST algorithm, our experiments show that ASTS has improved in string-wise operation environment and also performs well in character-wise environment.

Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e., the same rank is adopted to describe all users/items. However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy. In this paper, a mixture-rank matrix approximation (MRMA) method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks. Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA. Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

This study builds upon the 30-years HCI research of collaborative writing and focuses on users' experience of writing together in today's context. By interviewing 30 participants from both academia and industry, the paper examines how people write together using today's commercially available systems. The analysis focuses on the new co-editing capabilities (e.g., track changes) that are integrated into commercial tools and thus adopted by users widely in the last decade. These capabilities enable new ways of working together (e.g., directly edit the content at the character level at the same time), but users reported reluctance to fully commit to these new working styles. We thus systematically analyze users' rationales of why they do not want to write together while they are writing together with other. We argue that the development of collaborative writing tools is far from finished and these findings provide insights for the design of technology, and suggest future directions for research.

Nowadays people have many overlapping relationships across different social network sites. Will the communication frequency of a relationship on a focal SNS increase or decrease after the corresponding parties begin interacting with each other on a new SNS? What kinds of relationships and parties on the focal SNS are more robust to relationship overlapping across different sites? We conducted a case study on two Chinese popular social network sites (Weibo and Douban). Our results indicate that relationships' interactions on a new SNS have negative effect on the parties' communication frequency on the focal SNS. The communication frequency of older relationships on the focal SNS is more susceptible to the influence of the interacting on the new SNS, while relationships with more common groups and more extensive posts are less likely to be influenced. Our findings imply opportunities for SNS designers to strengthen their existing fragile ties and help users to build much more robust relationships.

In online collaboration projects, conflicts often arise in the peer review process, due to the disagreement over whether one's contribution should be accepted. These conflicts generally have detrimental effects on contributors' continuing participation in the community. Few studies have investigated how to manage these conflicts effectively. This paper aims to examine the effectiveness of three strategies - rational explanation, constructive suggestion, and social encouragement - in managing conflicts. In an analysis of 170 online software development projects, we investigated how different conflict management strategies aimed at handling contributors' arguments during the peer review process influenced their subsequent participation in the projects. The results show that (i) conflicts significantly increase contributors' likelihood of leaving the communities; (ii) neither rational explanations nor social encouragement could reduce the negative consequences of conflicts; (iii) only constructive suggestions have a positive effect in retaining the contributors.

More than 350 million people worldwide suffer from depression. Major depressive disorder has a hugely negative impact on psychological well-being, work, and family life. Yet culture may shape how depressed patients interpret their symptoms, choose treatments, and behave. This paper reports a case study, including participant observations and interviews, of the Chinese online depression community, SunForum. Our findings reveal that Chinese cultural beliefs (e.g., the power of inner self-control) and Chinese beliefs about traditional medicine (e.g., the integrated body-mind relationship) significantly affect patients' understandings of depression, illness management, and social interactions. These beliefs create problems of understanding depression in society - including family members, friends, co-workers, and others - and present various challenges for depressed patients who can become marginalized, suffer discrimination, and lose their jobs. We draw implications for how Chinese society as a whole may respond to the misunderstanding of mental illness and the raising of public awareness. We also propose specific social media design to support depressed patients as they seek online information and social support.

Smart environment has been evolved as a hot research topic with the development of machine learning algorithms and wireless communication technologies. Existing smart home solutions usually need inhabitants to operate device controllers directly. However, for people with disabilities, it is inconvenient and difficult to perform such manual operations. Therefore, it is important to develop automatic and intelligent services to reduce operation inconvenience and improve comfort level. In this paper, an Improved Hidden Markov Model (IHMM) is presented to support personalized behavior prediction for people with disabilities. The model can learn behavior patterns of users and provide services to inhabitants automatically. Moreover, by breaking the time invariant hypothesis in Hidden Markov Model, we incorporate time information as positions of states, and develop a temporal state transition matrix to replace the fixed state transition matrix to demonstrate the probabilities of state transitions. As a result, different values of daily temperature sections are characterized and identified as hidden variables, which guide user activities. Evaluation of proposed work has shown that IHMM improves the prediction accuracy by at least 10% compared to the traditional HMM.

With the development of information technology, a growing number of users begin to use Internet as a platform to plan their itinerary collaboratively. During the online itinerary planning process, collaboration plays an increasingly significant role. As the number of users grows, it is challenging to support more and more participants to plan itineraries online collaboratively, due to the requirement of responsiveness and data consistency. Traditional concurrency control methods (e.g. lock-based protocols, etc.) are costly because their primary copies are on a dedicated server and network latency, and concurrent operations violate CCI model. Address Space Transformation (AST) proves to the high performance in addressing the issue of interactive responsiveness and consistency maintenance. In this paper, a novel AST based approach is presented to support collaborative itinerary planning. We report how we address the emerging consistency maintenance challenges in directed graph of online itinerary planning. With this method, users are allowed to participate and leave itinerary planning tasks anytime. Moreover, collaborative itinerary planning also can be modified in real time with data consistent guarantees. A system called CLIP is implemented to evaluate its performance. Experimental results demonstrate the benefit and performance of this approach over the existing methods.

Online question and answer (Q&A) sites, which are platforms for users to post and answer questions on a wide range of topics, are becoming large repositories of valuable knowledge and important to societies. In order to sustain success, Q&A sites face the challenges of ensuring content quality and encouraging user contributions. This paper examines a particular design decision in Q&A sites-allowing Wikipedia-like collaborative editing on questions and answers, and explores its beneficial effects on content quality and potential detrimental effects on users' contributions. By examining five years' archival data of Stack Overflow, we found that the benefits of collaborative editing outweigh its risks. For example, each substantive edit from other users can increase the number of positive votes by 181% for the questions and 119% for the answers. On the other hand, each edit only decreases askers and answerers' subsequent contributions by no more than 5%. This work has implications for understanding and designing large-scale social computing systems.

This paper tells a story of DIY (do it yourself) making that does not neatly fit more familiar narratives of making: as individual empowerment, as a democratizing force, and as technoscientific innovation. Drawing on ethnographic research with a collective of elderly electronic hackers in China, we provide insights into the socio-technical and politico-economic processes of hacking and making. This paper examines how the activity of making functioned for elderly DIY enthusiasts as way of remaking and reliving the past and as a means for expressing class belonging and citizenship. We show that making and hacking is not practiced in a void independent of social, political or economic forces. Rather, making unfolds in relation to, and is contingent on, societal norms and specific techno-cultural histories. As much as hacking empowers certain people, it excludes others and functions as a site for the exercise of power and social distinction making.

Scientific research has become collaborative activities among scientists. Many scientists share data and scientific workflows with each other to facilitate scientific research. However, during using shared scientific workflows, it's hard for scientists to figure out suspicious data and processes, i.e., scientific workflow diagnosis. In this paper, we propose an approach for diagnosing scientific workflows based on the data provenance technology and reusing of diagnosis information. The main innovation of this paper is twofold: 1) By using data provenance technology, we deduce integrated diagnosis information of scientific workflows with the help of scientists' understanding of data; 2) We propose a novel model to preserve diagnosis information, and design a method to reuse the diagnosis information. We implement a prototype system based on the Kepler scientific workflow management system.

As Sina Weibo is becoming one of the most popular online social networking platforms in China, there is growing need in extracting knowledge and analyzing user behavior out of it. Influential user discovery has been quickly identified as an important application for better marketing and advertising purposes. In order to analyze user influence, we construct a user influence actor-network based on Actor-Network Theory, in which both users and users' microblogs are seen as actors and their interactions are treated as networks. User influence is then evolved into network effects caused by both microblogs and interactions, which are characterized as the Quality Index and Popularity Index of microblog respectively that can be well evaluated through models proposed in this paper. Furthermore, by observing that one user may show distinctive influence in different domains, we build our model domain specifically. With full consideration to quality, popularity and domain categories of users' microblogs, we propose a novel domain-specific user influence measurement, ANTRank, in Sina Weibo based on Actor-Network Theory. Experimental results show that the ANTRank performs well with the user influence evaluation.

With the development of web 2.0, online community websites have become one of the most interactive ways for disabilities to communicate with others and understand the world. However, new disabled users may sometimes find it difficult to learn about existing communities and the topics they feel interested in are drastically different from normal users. On the other hand, influential users have already developed their impact in different topics within communities and are hence able to help disabilities quickly adapt to the communities. As the result, a method that can recommend influential users in diverse topics is necessary. However, traditional approaches to identify influential users are not very comprehensive and accurate without the consideration of topic diversity. In this paper, we present an evaluation index system and a SVD based influence model which can evaluate influence comprehensively. The discussions in online communities are then classified by the topics that attract disabilities most. Finally, a combined influential users recommendation with diverse topics is proposed. We conduct experiments on an online disability community dataset and results show that our approach significantly outperforms other traditional methods.

System design for seniors often focuses on the decline of their biological capabilities and social connectedness. This approach has been challenged as too simplistic to capture what it really means to be senior. This paper presents a qualitative study of 17 seniors in urban China (age ranging from 50s to 70s), who have adopted and incorporated ICT into their daily lives. Findings from this study show that the ways in which seniors attend to ICT are not simply shaped by changes in health or other wellbeing, but also by their life attitudes, value systems, relationships to younger generations as well as historical specifics during their coming of age. This paper contributes by showing that 1) what it means to be senior is shaped from within a whole social ecology of past and current experiences, values and interactions; 2) senior identities are not fixed, but continuously negotiated, articulated and enacted through ICT; 3) social interaction and access of technologies are highly intertwined.

Commenting systems play increasingly important roles in the interactive web applications. Meanwhile, more and more web applications are visited on mobile devices. However, the intermittent connection of mobile networks and resource limitation of mobile devices pose great challenges, mainly in terms of interactive responsiveness and data consistency. In this paper, we present the first work of partial replication solution based on collaborative editing techniques, which can address the issues of local responsiveness and resource limitation on mobile commenting systems. We report how we address the consistency maintenance challenges that come with the partial replication approach. With this approach, users are allowed to smoothly comment anywhere anytime. The comment thread can be incrementally updated and automatically synchronized with strong data consistency guarantees. We implemented a system prototype called Hydra and evaluated it on a real data set.

Microblogging services, such as Sina Weibo and Twitter, provide a platform on which everyone can easily and quickly get and spread information. While the side effect is that not only valuable information spreads on the platform but also rumors. Rumors, as unconfirmed or false information, spread even faster through social networks and do much more harm to both online and offline society. Traditional technical approaches, like censorship, deleting posts and accounts cannot effectively reduce the impact caused by rumors on microblogging platforms. In this paper, we applied social translucence, a sociotechnical approach to design Sina Weibo Analysis Plug-in (SWAP), which includes a series of strategies to help users check the essential social evidences that would influence users' perceptions on microblog credibility. Our contribution is that we provide a simple and practical approach to support coherent and accountable behaviors on Sina Weibo.

With the prevalence of cloud services, more and more editing tools are moving from client to cloud for collaboration. Google Docs, Microsoft Office Web Apps, Zoho Office Suite are such representative cloud backed co-editing services. People from different organizations may choose different service providers, which implies the needs of cross-cloud document collaboration. However, it is hard to make different collaborative editing services interoperate with each other. On the one hand, it is not realistic to let all service providers comply to a unified standard and provide interoperation interfaces. On the other hand, the challenge of consistency maintenance in real-time document synchronization still exists. These make interoperability one of the most significant collaboration barriers in online collaborative editing. This paper presents a transparent approach called Hermes to interoperating between heterogeneous collaborative editing services in the cloud. Users are allowed to use their familiar services to participate in the cross-cloud document collaboration. Service providers do not need to put any effort to interoperate with other services. Our approach is validated by a system prototype.

In theory, software, like other digital artifacts, can be freely copied and distributed. In practice, however, its effective flow is conditioned on various technical and social factors. In this paper, drawing on ethnographic work primarily with a bio-informatics research team in China, we report on meanings of scientific software sharing as embedded in social practices of learning, apprenticeship, membership, publication, and reputation. We illustrate that while free flow is important, boundary management is equally important for the effective travel of software to its appropriate destinations. Our study highlights a number of issues that are important to consider for effectively supporting sharing and collaboration in science.

Content consuming and sharing are two most important user activities in social networking sites (SNSs). Lots of studies have been conducted on content recommendation using users' common interests. However, little has been done to help users to select friends and share content within their social networks. In this paper, we contribute a recommendation framework AOPUT to recommend both content and friend list for sharing to users leveraging content and social information in SNSs. It consists of two recommendation components: Recder and ShareAider. Recder generates content recommendations by connecting users with common interests. An improved Jaccard similarity is proposed to improve the Collaborative Filtering (CF) recommendation quality. ShareAider recommends a friend list to users when they want to share content with their friends. CF method and a social-based method are compared and the combination of them are explored to achieve better results. AOPUT is evaluated on a real world social network. The experimental results show that (1) Recder can provide better recommendation quality than the traditional CF method thanks to the improved Jaccard similarity; (2) social-based method performs better than CF since the sharing behavior in SNSs are highly dominated by users' social preferences, and the combination of these two methods performs better than each of them individually.

Dependency Graph (DG) is widely used in 3D design systems to represent the relationships among inner objects of the document. In order to transform the single-user application, which uses DG as the document model, into groupware system, OT is usually adapted to the new environment and five types of dependency conflicts are proposed and resolved in the literature. However, in some scenarios where loops are not allowed in the DG model, there are also two other conflicts named as cross-dependency conflict and transitive -dependency conflict, which may generate inconsistency document status or intention violation. This paper describes the definitions and detection rules of these two special conflicts and contributes a multi-version strategy to solve them. Conflict resolution strategies are detailed and examples are illustrated for their understanding.

As a form of collaborative work, sharing plays an important role in scientific research. Scientists share their discoveries with others by the ways of publishing paper, data and software. It promotes the development of research. With the increasing number of scientific software and more and more open source communities springing up, software selection becomes an urgent issue in sharing. In this paper, from the users' perspectives, we first propose a collaborative provenance model, which is designed to record the collaborative information between users and software. Then, drawing on ethnographic method, we explore the elements influencing the choice of users, and quantify them. On that basis, an algorithm is introduced to select the software for each process in workflow. For the whole workflow, a composition selection algorithm is presented.

Feedback on resource consumption is often explored as a way to raise awareness and saving resources. This paper reports findings from a user study of a feedback system deployed in a Chinese university dormitory with a prepaid electricity system, a context different from the more common domestic setting in the West explored in prior research. With this work, we move beyond resource conservation and draw attention to an often-neglected aspect of infrastructural work -- the work to ensure the smooth and continuous supply of resources from end users. This paper examines the ways in which people attend to electricity through what we term collective infrastructural work, i.e. people perceive electricity as a marginal concern, and yet invest time to maintain it collectively. We draw out a number of implications for design and evaluation from this work.

This paper examines issues of digitality and materiality of new media, grounded in a study of online TV watching in China. Particularly, by looking at how people make choices and decisions regarding TV watching in everyday life, we highlight material and digital properties of new media TV, and how they support and condition actions and interactions around them. The study illustrates that materiality and digitality are complementary, instead of one substituting the other, and are highly intertwined in the hybrid media environment around which meaningful experiences are conditioned and produced. It also suggests that an analytic distinction between materiality and digitality is fruitful in unpacking the complex relations between media technologies and social experiences.

This paper discusses issues of infrastructure and user experiences based on an empirical study of the online gaming platform ArcOnline in China. ArcOnline has allowed millions of users to play and watch arcade games over the Internet. What the study of ArcOnline helps bring to the fore are the infrastructural aspects of user experiences, mainly because the high demand for responsiveness and resources of arcade gaming has made infrastructures visible. In this paper, we present our findings of how the experience of playing arcade games changes with different socio-technical infrastructures, the arcade room and the online environment, highlighting how the form of the infrastructure shapes experience and collaboration, as well as how new social infrastructures are emerging through the interplay between the game, the platform, the community and the media ecology. We end by discussing the importance of considering infrastructure in understanding and designing new media experiences.

Provenance plays a fundamental role in e-science to keep track of the data processing execution, evaluate the data quality, reproduce the analysis results, and especially share and re-use workflows. How to take full advantage of provenance to help scientists discover, match and select scientific workflows is a challenging work. Although some studies have been done to model, store, and query scientific workflows, little is done to build practical systems to support workflow matching and discovery. In this paper, we devise and implement a Provenance-Based Workflow Matching and Discovery System (PWMDS) for task-based pipelines in a proteomics data analysis platform called CoPExplorer to address the above challenge. With the proposed novel provenance model and workflow matching & discovery algorithms, PWMDS can provide scientists a ranked list of suitable service candidates for their specified workflows, and initial experiments demonstrate its effectiveness.

Current Web 2.0 services are making mass collaboration a reality. Using a Web browser, people can participate in cooperative work anytime, anywhere from any computing device as long as there is an Internet connection. Lying in the heart of some well-known services is an optimistic consistency control technique called operational transformation (OT). This paper proposes TIPS, a novel sync protocol that adapts OT for Web 2.0 applications. Based on a recent theoretical framework called ABT, it ensures not only convergence but also the right object order for linear documents. Designed to address the HTTP style of communication, TIPS allows clients to sync with the server by independent time intervals and dynamically join and leave at any time. When processing do operations, its time complexity is linear in the total number of operations generated by all clients during one server interval and independent of the size of history. TIPS is efficient for supporting a spectrum of (near-)realtime to asynchronous collaboration editing tasks.

This paper presents a collaboration transparency system that allows for distributed sharing of legacy collocated video games in real time without modifying the original games. Our system has attracted two million registered players since it was launched in late 2008. As the system went beyond a research prototype and reached a large number of real users, we observed performance issues that were mainly caused by unpredicted networking latencies over the public Internet and opportunistic distinction of roles such as active players and passive observers. To address these issues, we propose a novel hybrid consistency control approach that combines the merits of optimistic methods, which help achieve interactivity, and pessimistic methods, which guarantee consistency. As a result, the system can tolerate network round-trip time up to 230 ms, improving the latest work by 43.7%. In addition, the system can support over 1,000 simultaneous observers when a video game is played over the Internet.

In a cloud computing environment, a great amount of services require to be deployed into the cloud, which may lead to the redundancy and complexity of cloud-services, and ever-increasing resource consumption. Current researches try to optimize the increasing number of services with the support of buffer pool and deployment optimization algorithms. Buffer pool here performs the crucial task of storing the deployment requirements for optimization. However, issues like when to enforce the flush and which stored objects should be flushed out still lack comprehensive consideration. To address these problems, we propose an improved collaborative dynamic double buffer pool in this paper. By classifying different deployment requirements and flush fashions, we select a threshold to determine when to enforce the flush. According to this flush time, we can select the to-be-optimized objects dynamically along with necessary collaborative interaction with the service owner. Meanwhile we present the algorithms of the buffer pool flush mechanism, and the improved service deployment optimization approach. In the end, we illustrate the effectiveness and feasibility of our approach through a demonstration which guarantees relatively high optimization efficiency.

With the explosive growth of online social communities and massive user-generated content, privacy-preserving recommender systems, which identify information of interest to individual users without disclosing personal interests to other parties, have become increasingly important. Collaborative filtering (CF), a widely used recommendation technique, recommends content that similar users have liked. As a result, CF-based recommender systems may expose sensitive personal interest information. This is demonstrated by a privacy attack model we present that targets online social communities. To solve this problem, we propose an interest group based privacy-preserving recommender system called Pistis. By identifying inherent item-user interest groups and separating users' private interests from their public interests, Pistis can make recommendations based on aggregated judgments of group members and local personalization, thus avoiding the disclosure of personal interest information. Pistis has been deployed and evaluated in an online social community with over 63,000 users, 20,000 daily posts, and 180,000 daily reads. Compared with two representative CF-based methods, our evaluation results demonstrate that Pistis achieves better performance in privacy preservation, recommendation quality, and efficiency.

Transparent Adaptation (TA) technology is a strategy to transform single-user applications into groupware system without modifying the original source codes. There are two key issues of TA technology: data model transformation and operation adaptation. The paper introduces to map the two dimensional CAD document into layered document model, modifies AST consistency maintenance strategy to layer-AST to adapt to the new model; gives primitive operation definitions in CAD environments, and uses application APIs to capture the interface operations. Efficiency analysis of the layer-AST is also given, showing the improved performance of the algorithm.

In service oriented cloud, in order to meet the different users' service requirements, the cloud venders need to manage the service applications' deployment effectively first. During the deployment process, the deployment costs and the influence of service deployment to the cloud are very important issues to be paid attention to, which lacks in current research. In this paper, we design a novel optimization approach for service deployment to address above issues, which works in a service deployment framework. The approach can improve the deployment efficiency and reduce the deployment costs. Atom-services, the basic units of service application, are partitioned to different service families with its compatibility and installation policy. We present three algorithms to automatically standardize the installation expressions, simplify and optimize the installation expression series during the service deployment process respectively. An analysis about the service deployment result is given in the paper and the experiment data demonstrates the approach's efficiency.

This paper studies online quality measurement in cloud computing environment. The paper analyzes concentration measure evaluation method of the current software quality evaluation system. As is known to all, the existing evaluation technology uses concentrated evaluation before run-time. By testing and analyzing results, the quality is obtained. In this paper, by analyzing the features in cloud computing environment, we consider that service-oriented evaluation should be mainly based on runtime online measurement. The paper puts forward agent-based online measure infrastructure, which is evaluated by distributed assessment in service computing environment A quality model and corresponding online data collection strategy are described. In this approach, the previous quality evaluation, which is concentrated and simulating in a simulated environment, is substituted. Then, a novel measure method of quality data acquisition, which is based on distributed agent technology, is established. So, during online service operation, the obtained data can make measurement results to be accurate and credible.

In order to bridge the gap between complex operations and atomic operations in CAD groupware, adapter tools are introduced to decompose complex operations into atomic ones by breaking up the former according to different steps and different objects. However, during the decomposition process, because of the loss of the relationships between the original operations and the atomic ones, the intention of the Undo operation of the complex operation may be violated by concurrent operations coming from remote sites since traditional Undo schema is based on the decomposed operation history buffer and the undo unit is the atomic operation. The paper discusses the problems of Undo pre-condition and Undo conflict, and introduces Original Op history buffer and Bi-Vector strategy to maintain the relationship between the original operations and the decomposed ones. Moreover, the paper gives detailed description about the work flow and main algorithms of complex Undo operations. Examples are given to demonstrate the effectiveness of our strategy.

Two kinds of operations are discovered in CoAutoCAD system: immediate one and step-wise one. The execution of the former (such as Line, Box etc.) is completed in one step while the latter(such as Mirror, Array) takes some continuous or discrete steps and the number of the steps can even not be predicted in some scenarios (such as Copy-Paste). So, during the execution process, the intention of the step-wise operations may be violated by concurrent operations coming from remote sites. This paper discusses the phenomenon and proposes to maintain the semantic intention of operations by introducing a novel approach called CLPF (Check Last Predict Future) to solve the violation happened before while predicting the future using the newly-updated information. In order to get the execution format of causally-ready operations, GOTO algorithm has been extended with VT function reflecting the influence of one operation on the other one.

A fuzzy classification method for cooperative design documents of manufacturing based on domain ontology and document structure tagging is presented in this paper. It employs the hierarchical structure of domain ontology and semantic relations between concepts to partition and tag the design documents. Every concept in domain ontology is weighted by the position importance of feature words and the distance between each feature word and the concept itself. As a demonstration, this method is applied in the shipping engineering, which enhances the accuracy of the design documents classification.

Service-oriented computing (SOC) emerging as a new computing diagram that supports the rapid and low-cost development of distributed applications by composing platform-independent, coarse-grained and loosely coupled services. With the prevalence of service-oriented system, how to build survivable applications in the immature research field of service-oriented system engineering (SOSE) has become a big challenge. An approach of configuration management based on service dependency is presented in this paper to analyze and optimize the survivability of applications. Three dimensions, i.e. distance, strength and criticality, are defined to describe the survivability of service dependency. A directed acyclic graph called weighted system configuration graph (WSCG) is proposed to model the survivability of an application, where each direct dependency between two services is assigned a weight which is calculated based on the three dimensions. Then we give an algorithm in WSCG to obtain the maximal survivability system configuration from redundant service configurations. The mobile video conference (MVC) scenario is illustrated to show the effectiveness and the soundness of our approach and algorithms. At last, the conclusion is drawn with future work given.