Existing Text-Video Retrieval (TVR) methods predominantly rely on global frame representations, often disregarding the fine-grained temporal variations required for precise patch-level alignment. This is critical as video motion is inherently spatially localized; consequently, coarse frame-level modeling tends to be dominated by static backgrounds, overshadowing salient action cues. To address this limitation, we propose TRFG, a novel framework for text-video retrieval that addresses the challenges of modeling Temporal Reasoning and Fine-Grained cross-modal alignment. First, our Temporal Difference module captures frame-to-frame variations at the patch level, effectively suppressing static background noise to highlight "active" motion regions. Second, these differential signals are synthesized via a Temporal Aggregation module to form a coherent representation of the event’s trajectory. Finally, to ensure precise semantic matching, a fine-grained interaction module aligns these dynamic video tokens with textual details. Extensive experiments on MSRVTT, ActivityNet, and DiDeMo demonstrate that TRFG achieves state-of-the-art performance across multiple backbones and retrieval tasks. Ablation studies confirm the complementarity and generalizability of both modules, underscoring the importance of explicit temporal modeling and fine-grained interaction in bridging the modality gap.

Visual Document Retrieval (VDR) requires representations that capture both fine-grained visual details and global document structure to ensure retrieval efficacy while maintaining computational efficiency. Existing VDR models struggle to balance effectiveness and efficiency when processing high-resolution documents: they often either lose fine-grained information or generate an excessive number of visual tokens, resulting in significant indexing overhead and high retrieval latency. In this work, we rethink the visual encoding mechanism and propose a new X-VisEmb paradigm that progresses from multi-resolution sampling and encoding, through cross-granularity feature fusion, to adaptive representation distillation. A preliminary study validates its feasibility and effectiveness in capturing complementary visual cues at varying scales. Building on the insights, we develop MURE , a novel framework that employs VLMs as a hierarchical multi-resolution encoder, integrates resolution-level Matryoshka representation learning (RMRL) for effective feature fusion, and applies a semantic-aware hierarchical clustering mechanism for visual token compression. Experiments on two widely used VDR benchmarks show that our MURE framework consistently beats strong baselines. Furthermore, it significantly outperforms ColPali with only 50% of its visual token budget.

Provider exposure fairness is crucial for sustaining a healthy content ecosystem and preventing monopolization in recommender systems. Yet, most existing methods either incorporate fairness constraints during model training, requiring expensive retraining when fairness objectives change, or rely on post-hoc reranking with fixed criteria, which lacks adaptability to diverse fairness requirements. To overcome these limitations, we propose Post-hoc Fairness Adaptation (PFA), a lightweight framework that equips a frozen recommender with a fairness adapter, enabling flexible fairness control without retraining the backbone model. Specifically, the fairness adapter learns personalized additive score adjustments from user–item embeddings, which are injected into the original ranking scores to steer provider exposure toward fairness. To train the adapter, we minimize the Kullback-Leibler (KL) divergence between the actual and the target fair exposure distributions. However, this global objective implicitly treats all providers equally, ignoring structural disparities such as imbalanced provider group sizes and heterogeneous exposure within groups. Consequently, fairness may appear satisfied at an aggregate level while severe inter-group and intra-group exposure imbalances persist, undermining practical fairness. To address this, we design Hierarchical Exposure Fairness Alignment (HEFA), which explicitly balances inter- and intra-group provider exposure disparities, enabling flexible adaptation to diverse fairness requirements. To mitigate potential accuracy degradation, PFA jointly optimizes HEFA with a differentiable NDCG loss, enabling end-to-end fairness optimization while preserving ranking quality. Extensive experiments on three public datasets demonstrate that PFA achieves substantial fairness gains with negligible accuracy loss, consistently outperforming strong baselines. Code is available at https://github.com/Tam-JQK/Post-train.

Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing Bio-RE methods struggle with cross-sentence inference, which is essential for capturing relations spanning multiple sentences. Moreover, previous methods often overlook the incompleteness of documents and lack the integration of external knowledge, limiting contextual richness. Besides, the scarcity of annotated data further hampers model training. Recent advancements in large language models (LLMs) have inspired us to explore all the above issues for document-level Bio-RE. Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG). First, we introduce the Iteration-of-REsummary (IoRs) prompt for solving the data scarcity issue. In this way, Bio-RE task-specific synthetic data can be generated by guiding ChatGPT to focus on entity relations and iteratively refining synthetic data. Next, we propose ADRCM fine-tuning, a novel fine-tuning recipe that establishes mappings across different documents and relations, enhancing the model’s contextual understanding and cross-sentence inference capabilities. Finally, during the inference, a biomedical-specific RAG approach, named CUI RAG, is designed to leverage CUIs as indexes for entities, narrowing the retrieval scope and enriching the relevant document contexts. Experiments conducted on three Bio-RE datasets—GDA, CDR, and BioRED—demonstrate the state-of-the-art performance of our proposed method by comparing it with other related works.

Rating prediction is a classic task in recommendation systems, aiming to accurately estimate user ratings for various items. Historical ratings typically exhibit a non-uniform distribution, leading recommendation models to favor predicting high-frequency ratings. We refer to the inconsistency between predicted ratings and users' true preferences caused by non-uniform rating distributions asrating bias. To mitigate this bias, existing studies capture various interaction behavior patterns and employ knowledge distillation techniques to improve the network's ability to model user preferences. However, due to the model being trained on datasets with non-uniform rating distributions, the rating bias may propagate through the knowledge distillation process across different behaviors, thereby contaminating the modeling of users' true preferences. To this end, we propose a novelAdversarial Counterfactual Distillation(ACD) framework for the rating prediction task, aimed at eliminating rating bias. Specifically, we design aCounterfactual Distillation Modulefrom a causal reasoning perspective to facilitate knowledge transfer across various interaction behaviors while concurrently mitigating bias contamination. Furthermore, we introduce anAdversarial Debiasing Moduleto dynamically adjust the debiasing strength, ensuring that the model maintains an optimal balance between effective knowledge transfer and bias mitigation. Extensive experiments demonstrate the superior performance of our proposed ACD framework. The complete code is publicly available athttps://github.com/hfutmars/ACD.

Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, CAT enables personalized learner modeling and has attracted substantial attention. Despite this progress, most existing works focus primarily on improving diagnostic accuracy, while overlooking the selection bias inherent in the adaptive process. Selection Bias arises because the question selection is strongly influenced by the estimated proficiency, such as assigning easier questions to learners with lower proficiency and harder ones to learners with higher proficiency. Since the selection depends on prior estimation, this bias propagates into the diagnosis model, which is further amplified during iterative updates, leading to misalignment and biased predictions. Moreover, the imbalanced nature of learners' historical interactions often exacerbates the bias in diagnosis models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. First, we retrieve balanced examinees with relatively even distributions of correct and incorrect responses and use them as neutral references for biased examinees. Then, mixup is applied between each biased examinee and its matched balanced counterpart under label consistency. This augmentation enriches the diversity of bias-conflicting samples and smooths selection boundaries. Finally, extensive experiments on two benchmark datasets with multiple advanced diagnosis models demonstrate that our method substantially improves both the generalization ability and fairness of question selection in CAT.

Vision-language pre-training (VLP) models exhibit exceptional generalization capabilities across diverse vision-language (V+L) tasks. However, studies reveal their vulnerability to carefully crafted multimodal adversarial examples. Notably, state-of-the-art attacks like Co-Attack (white-box) and SGA (transfer-based) demonstrate alarming success rates, posing critical security threats to VLP models. Current defense mechanisms against such multimodal attacks remain insufficiently explored. To address this challenge, we propose Joint Adversarial Purification (JAP), a novel defense framework that synergistically eliminates adversarial perturbations across modalities through cross-modal interaction. Our approach harnesses cross-modal semantic synergy to jointly purify adversarial perturbations: Generative denoising establishes visual-semantic anchors through diffusion processes, while purified linguistic cues conversely enhance visual perturbation filtering, forming a self-reinforcing defense cycle. Extensive experiments demonstrate that JAP effectively mitigates adversarial threats from both white-box Co-Attack and transfer-based SGA, significantly outperforming existing unimodal defense baselines. This work establishes a new paradigm for securing VLP models against multimodal adversarial attacks.

Social media platforms have become major sources of misinformation, primarily manifesting as rumors and fake news. While existing detection methods demonstrate good accuracy, they still exhibit numerous issues related to confidence calibration. Existing methods often lack robustness due to insufficient consideration of adversarial attacks, which compromises their security and introduces greater uncertainty in predictions. Moreover, the predominant focus on improving model performance has led to the neglect of prediction risk, resulting in numerous overconfident and unconfident predictions. To address the above issues, we present a novel temperature-scaled ensemble frame with robust adversarial training(RATE) for calibrating misinformation detection models. We employed the fast gradient sign method for adversarial training to smooth the predictive distribution, defined an ensemble framework with independently trained backbone networks to reduce uncertainty, and applied temperature scaling during validation to align confidence outputs with the true distribution. Through extensive evaluation of context-based rumor detection and content-based fake news detection models across six diverse datasets, we reveal critical issues in the models' predictive confidence calibration. Experimental results demonstrate that RATE effectively addresses confidence bias while maintaining predictive accuracy, offering a universal solution for constructing reliable misinformation detection systems.

Graph-based social recommender systems have demonstrated great potential in alleviating data sparsity by leveraging high-order user influence embedded in social networks.However, most existing methods rely heavily on the observed social graph, which is often noisy and includes spurious or task-irrelevant connections that can mislead user preference learning.Identifying and removing these noisy relations is crucial but challenging due to the lack of ground-truth annotations.In this paper, we approach the social denoising problem from the perspective of graph invariant learning and propose a novel approach, Social Graph Invariant Learning(SGIL).Specifically, SGIL aims to uncover stable user preferences within the input social graph, thereby enhancing the robustness of

Personalized learner modeling uses learners' historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD).This is essential for web-based learning services such as learning resource recommendation and adaptive testing.However, prior studies have shown that CD models may unfairly correlate learners' abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes.While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical.Furthermore, the presence of multiple sensitive attributes complicates fairness improvements.In this paper, we explore how to achieve fair personalized learner modeling without * Min Hou is the corresponding author.

Generating temporally smooth and high-resolution videos is a crucial objective in talking face generation tasks. Diffusion-based generative models have emerged as a prime choice for these tasks due to their ability to produce high-quality outputs. To mitigate the impact of stochasticity in the diffusion process, recent research has predominantly utilized self-attention layers to extract temporal features, ensuring temporal consistency in the generated videos. However, self-attention mechanisms have computational complexity that scales quadratically with video length, leading to high computational costs. This limitation poses significant challenges when attempting to generate longer video sequences using diffusion models. To address this challenge, we propose Talking-DiSSM, an end-to-end method for generating audio-driven talking face videos using State-Space Models (SSMs). This novel framework for conditional video diffusion modeling integrates Bidirectional State-Space Models (Bi-SSM) as temporal modeling modules with linear complexity, effectively capturing complex sequential temporal information and intra-batch sequential interdependencies in videos. Additionally, we employ a simple yet effective batch-overlapped sampling strategy to process input video clips, constructing inter-batch correlations while incorporating reference face clips and landmarks as conditions to ensure stability in the generation process. Extensive experiments demonstrate that Talking-DiSSM generates temporally consistent, high-quality, and identity-preserving talking face videos synchronized with the driving audio, achieving state-of-the-art results compared to existing models.

Cold-start recommendation is a long-standing challenge when presenting potential preferred items to new users. Most empirical studies leverage side information to promote cold-start recommendation. In this work, we focus on cross-domain cold-start recommendation, which aims to provide suggestions to those non-overlapping users who have only interacted in the source domain and are viewed as new users in the target domain. Pre-training and then mapping is the common solution for the cross-domain cold-start recommendation. The former learns domain-specific user preference, and the latter transfers preference knowledge from the source to the target domain. Despite the effectiveness, we argue that current mapping-based methods still have the following limitations. First, current mapping functions fail to fully consider the similarity of user behavioral patterns, either common transfer or personalized transfer mappings. Second, sparse supervision signals from the limited overlapping users, lead to insufficient mapping function learning for recommendation. To tackle the above limitations, we propose a novelMACDRmodel for cross-domain cold-start recommendation. Specifically,MACDRconsists of two elaborate modules: a Prototype enhanced Mixture-Of-Experts (PMOE) based mapping function and a Preference Distribution Alignment (PDA) enhanced optimization. PMOE is designed to balance the transfer patterns of common and personalized preferences, following the basis that similar users share similar preference transfer. Furthermore, to alleviate the sparse supervision issue, PDA is designed to explore the utilization of non-overlapping users in an unsupervised manner based on the prototype distribution alignment technique. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method.

The prevalent recommendation techniques explore the graph structure of interactions to alleviate the interaction sparsity issue for inferring users’ interests. These graph models focus on extracting local structural signals to model users’ interests, introducing grid-like distortion and ignoring the hierarchical tree-like structure when learning from the interaction graph. The learned interests lack significant hierarchical signals, resulting in suboptimal recommendation performance. In this article, we investigate geometric-augmented graph learning with hyperbolic and Euclidean geometries to delve into local structural and hierarchical knowledge from the interaction graph. A self-teaching network called geometric-augmented self-distillation (GASD) is proposed to transfer hierarchical knowledge from hyperbolic to Euclidean space. The transfer learning enables shrinking of the network into a primary student to implement effective and efficient inference in Euclidean space, preventing computational burden in hyperbolic space. Experiments on publicly available datasets demonstrate that the proposed GASD outperforms the state-of-the-art models, verifying the effectiveness and efficiency of knowledge transfer by self-distillation to aggregate knowledge adaptively for personalized recommendation.

Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-End Explainable Fake News Detection Network ( EExpFND ). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that EExpFND sets a new benchmark in performance. The code is available at https://anonymous.4open.science/r/EExpFND-F5C6 .

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable.

Text-guided image generative diffusion models achieve fast development on the generation and editing of high-quality images. To extend such success to video editing, some efforts combining image generation with video editing have been made, which however only achieve inferior performance. We attribute it to two challenges: 1) different from the static image generation, it is tricky for dynamic video information to ensure the temporal fidelity of motion consistency across different frames; 2) the randomness of the frame generation process makes it hard to continuously retain the similar spatial fidelity for the original detailed features. In this paper, we propose a new high-fidelity diffusion model-based zero-shot text-guided video editing network, called HiFiVEditor, which aims to conduct effective video editing with high fidelity of the original video's detailed and dynamic information. Specifically, we propose a Spatial-Temporal Fidelity Block (STFB) that enables the model to restore the spatial features by enlarging the spatial perceptual field to avoid loss of important information, and capture more dynamic information between different frames by using all frames for preserving temporal consistency to achieve better temporal fidelity. In addition, we introduce Null-Text Embedding to create a soft text embedding to optimize the noise learning process, so that the latent noise can be aligned with the prompt. Furthermore, to tune the video style and render it more realistic, we employ a Prior-Guided Perceptual Loss to constrain the prediction results to avoid deviating from the original video style. Extensive experiments demonstrate the superior video editing capability compared to existing works.

As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (such as misclicks or non-preferential behaviors), which will affect precise user preference learning. To overcome the noisy samples problem, a popular solution is based on dropping noisy samples in the model training phase, which follows the observation that noisy samples have higher training losses than clean samples. Despite the effectiveness, we argue that this solution still has limits. (1) High training losses can result from model optimization instability or hard samples, not just noisy samples. (2) Completely dropping of noisy samples will aggravate the data sparsity, which lacks full data exploitation.

With the emergence of social networks, social recommendation has become an essential technique for personalized services.Recently, graph-based social recommendations have shown promising results by capturing the high-order social influence.Most empirical studies of graph-based social recommendations directly take the observed social networks into formulation, and produce user preferences based on social homogeneity.Despite the effectiveness, we argue that social networks in the real-world are inevitably noisy (existing redundant social relations), which may obstruct precise user preference characterization.Nevertheless, identifying and removing redundant social relations is challenging due to a lack of labels.In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective.Specifically, we propose a novel Graph Bottlenecked Social Recommendation (GBSR) framework to tackle the social noise issue.GBSR is a model-agnostic social denoising framework, that aims to maximize the mutual information between the denoised social graph and recommendation labels, meanwhile minimizing it between the denoised social graph and the original one.This enables GBSR to learn the minimal yet sufficient social structure, effectively reducing redundant social relations and enhancing social recommendations.Technically, GBSR consists of two elaborate components, preference-guided social graph refinement, and HSIC-based bottleneck learning.Extensive experimental results demonstrate the superiority of the proposed GBSR , including high performances and good generality combined with various backbones.Our code is available at: https://github.com/yimutianyang/KDD24-GBSR.

Cognitive Diagnosis (CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in Intelligent Education. Due to the scarcity of student-exercise interaction data, most existing methods focus on making the best use of available data, such as exercise content and student information (e.g., educational context). Despite the great progress, the abuse of student sensitive information has not been paid enough attention. Due to the important position of CD in Intelligent Education, employing sensitive information when making diagnosis predictions will cause serious social issues. Moreover, data-driven neural networks are easily misled by the shortcut between input data and output prediction, exacerbating this problem. Therefore, it is crucial to eliminate the negative impact of sensitive information in CD models. In response, we argue that sensitive attributes of students can also provide useful information, and only the shortcuts directly related to the sensitive information should be eliminated from the diagnosis process. Thus, we employ causal reasoning and design a novel Path-Specific Causal Reasoning Framework (PSCRF) to achieve this goal. Specifically, we first leverage an encoder to extract features and generate embeddings for general information and sensitive information of students. Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained. Finally, we designed a multi-factor constraint to ensure the performance of fairness and diagnosis performance simultaneously. Extensive experiments over real-world datasets (e.g., PISA dataset) demonstrate the effectiveness of our proposed PSCRF.

Unpaired Image Captioning (UIC) is designed to describe an image without relying on matched vision-language training data. It is a challenging task since (1) the implicit and unpaired vision-language data nature of the training task limits the captioning model's ability to represent diverse scene representations, and (2) it is difficult for the captioning model to discern the intrinsic relationships among objects, potentially leading to misinterpretation of the image con- tent. To solve these issues, we propose pseudo content hallucination (PCH) to help the captioning model enlarge the perception of the ob- jects and capture the relations between the objects. Specifically, we select similar objects from different images as pseudo content and then hallucinate new visual content for training. This hallucinated content contains a similar scene but with a different representation, thus enriching the diversity of the training samples. Meanwhile, we utilize the relationships among these objects to improve the generated captions as a textual content hallucination and construct pseudo image-sentence pairs to refine the captioning model. These hallucinated sentences are beneficial for the captioning model as they enable the capture of additional semantics from the image, ultimately enhancing the sentence generation ability. Extensive experiments on the two benchmarks, i.e., MSCOCO, and Flickr30k, show the effectiveness of our method. The results show a significant improvement compared to the baseline in the MSCOCO dataset, with 1.5 increase in the CIDEr score.

The redundancy in videos and the quadratic scaling with input length of Transformer models lead to the need for sampling and selection from input videos.During the selection process, differentiable Top-K algorithms are employed to ensure an end-to-end training process.However, these methods not only restrict the level at which temporal information is captured but also introduce sorting noise and inaccuracies.In this paper, we revisit the keyframe selection strategy for VideoQA and propose a novel framework named Dual-Stream Keyframe Enhancement (DSKE) incorporating the enhancement of temporal granularity.To balance end-to-end sorting and hard ranking, we employ a dual-stream keyframe selection strategy by fusing the differentiable and non-differentiable results together to achieve a unified approach.One stream is based on the approximate ranking obtained from the differentiable Top-K algorithm, while the other stream utilizes the results obtained from hard ranking.We separately train decoders on the outputs of each stream and then combine the decoder results to predict the final answer.By integrating both stream results, DSKE effectively balances the inclusion of relevant information while filtering out noise.Additionally, we capture temporal variation information by incorporating a series of overlapping sliding time windows to enrich the temporal granularity.To evaluate the effectiveness of DSKE, we conduct experiments on the NExT-QA and AGQA benchmarks.The results demonstrate that our framework significantly improves the performance of VideoQA by effectively incorporating temporal components and enhancing the keyframe ranking process.

Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge number of new items are created rapidly over time. How to quickly provide recommendations for new items at the inference time is challenging. What's worse, real-world items exhibit varying degrees of modality missing(e.g., many short videos are uploaded without text descriptions). Though many efforts have been devoted to multimedia-based recommendations, they either could not deal with new multimedia items or assumed the modality completeness in the modeling process.

RGB-D cross-modal person re-identification (re-id) targets at retrieving the person of interest across RGB and depth image modalities. To cope with the modal discrepancy, some existing methods generate an auxiliary mode with either inherent properties of input modes or extra deep networks. However, such useful intermediary role included in generated mode is often overlooked in these approaches, leading to insufficient exploitation of crucial bridge knowledge. By contrast, in this article, we propose a novel approach that constructs an intermediary mode through the constraints of self-supervised intermediary learning, which is freedom from modal prior knowledge and additional module parameters. We then design a bridge network to fully mine the intermediary role of generated modality through carrying out multi-modal integration and decomposition. For one thing, this network leverages a multi-modal transformer to integrate the information of three modes via fully exploiting their heterogeneous relations with the intermediary mode as the bridge. It conducts the identification consistency constraint to promote cross-modal associations. For another, it employs circle contrastive learning to decompose the cross-modal constraint process into several subprocedures, which provides the intermediate relay during pulling two original modalities closer. Experiments on two public datasets demonstrate that the proposed method exceeds the state-of-the-arts. The effectiveness of each component in this method is verified through numerous ablation studies. Additionally, we have demonstrated the generalization ability of the proposed method through experiments.

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as queries to retrieve relevant logical descriptions and scenario descriptions from the corresponding memory modules, respectively. The retrieved results are then used to complement the process of the MWP generation. Extensive experiments and ablation studies verify the superior performance of our method and the effectiveness of each proposed module. The code is available at https://github.com/mwp-g/MWPG-DMR .

Multimedia-based recommendation (MMRec) models typically rely on observed user-item interactions and the multimodal content of items, such as visual images and textual descriptions, to predict user preferences. Among these, the user's preference for the displayed multimodal content of items is crucial for interacting with a particular item. We argue that users' preference behaviors (i.e., user-item interactions) for the modality content of items, beyond stemming from their real interest in the modality content, may also be influenced by their conformity to the popularity of items' modality-specific content (e.g., a user might be motivated to interact with a lipstick due to enthusiastic discussions among other users regarding textual reviews of the product). In essence, user-item interactions are jointly triggered by real interest and conformity. However, most existing MMRec models primarily concentrate on modeling users' interest preferences when capturing multimodal user preferences, neglecting the modeling of their conformity preferences, which results in sub-optimal recommendation performance. In this work, we resort to causal theory to propose a novel MMRec model, termed Multimodal Graph Causal Embedding (MGCE), revealing insights into the crucial causal relations of users' modality-specific interest and conformity in interaction behaviors within MMRec scenarios. Inspired by the colliding effect in causal inference and integrating the characteristics of real interest and conformity, we devise multimodal causal embedding learning networks to facilitate the learning of high-quality causal embeddings (multimodal interest and multimodal conformity embeddings) from both the structure-level and feature-level, yielding state-of-the-art performance. Extensive experimental results on three datasets demonstrate the effectiveness of MGCE.

Social recommendation provides an auxiliary social network structure to enhance recommendation performances. By formulating user-user social network and user-item interaction graph, modern social recommendation architecture is built on learning user and item embeddings into Euclidean space with graph convolution operations. However, the Euclidean space suffers structure distortion when representing the nature power-law distribution of graphs, leading to sub-optimal results for graph based social recommendation. Recently, some studies have explored the alternative of graph embedding learning into hyperbolic space, which can preserve the hierarchy of real-world graphs. However, directly applying current hyperbolic graph embedding models for social recommendation is non-trivial as two challenges: network heterogeneity and social diffusion noise. First, due to the semantic gap existing between social networks and user-item interactions, how to tackle the heterogeneity issue of social recommendation under hyperbolic formulation? Second, explicit modeling of social diffusion easily introduces noise for user preference learning, especially for those active users with amounts of interactions. To tackle the above challenges, in this paper, we propose aHyperbolic Graph Learning based Social Recommendation (HGSR)model. Firstly, we exploit social structure with hyperbolic social embedding pre-training, which could preserve the hierarchical properties of social networks. Secondly, we construct the heterogeneous graph based on user-item interactions and social networks, then treat the pre-trained social embeddings as an additional feature input for user preference learning. Such that, we combine explicit heterogeneous graph learning and implicit feature enhancement for the hyperbolic social recommendation, which can well tackle heterogeneity and social noise issues. We conduct empirical studies on four datasets, and extensive experiments demonstrate the effectiveness of our proposed model compared to state-of-the-art baselines.

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub-user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea is to achieve model fairness from a causal perspective. The remaining challenge is that we have no access to interventions, i.e., the counterfactual world that produces recommendations when each user has changed the sensitive attribute value. To this end, we first borrow the Rubin-Neyman potential outcome framework to define average causal effects of sensitive attributes. Next, we show that removing causal effects of sensitive attributes is equal to average counterfactual fairness in CF. Then, we use the propensity re-weighting paradigm to estimate the average causal effects of sensitive attributes and formulate the estimated causal effects as an additional regularization term. To the best of our knowledge, we are one of the first few attempts to achieve counterfactual fairness from the causal effect estimation perspective in CF, which frees us from building sophisticated causal graphs. Finally, experiments on three real-world datasets show the superiority of our proposed model.

In this article, we provide an overview of ACM TWEB’s Special Issue on Conversational Information Seeking. It highlights both research and practical applications in this field. The article also discusses the future potential of conversational information seeking technology.

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

Multimedia-based recommendation (MMRec) utilizes multimodal content (images, textual descriptions, etc.) as auxiliary information on historical interactions to determine user preferences. Most MMRec approaches predict user interests by exploiting a large amount of multimodal contents of user-interacted items, ignoring the potential effect of multimodal content of user-uninteracted items. As a matter of fact, there is a small portion of user preference-irrelevant features in the multimodal content of user-interacted items, which may be a kind of spurious correlation with user preferences, thereby degrading the recommendation performance. In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. Going beyond multimodal user preference modeling only using interacted items, we propose a novel model called Multimodal Counterfactual Learning Network (MCLN), in which user-uninteracted items' multimodal content is additionally exploited to further purify the representation of user preference-relevant multimodal content that better matches the user's interests, yielding state-of-the-art performance. Extensive experiments are conducted to validate the effectiveness and rationality of MCLN. We release the complete codes of MCLN at https://github.com/hfutmars/MCLN.

Review information has been demonstrated beneficial for the explainable recommendation. It can be treated as training corpora for generation-based methods or knowledge bases for extraction-based models. However, for generation-based methods, the sparsity of user-generated reviews and the high complexity of generative language models lead to a lack of personalization and adaptability. For extraction-based methods, focusing only on relevant attributes makes them invalid in situations where explicit attribute words are absent, limiting the potential of extraction-based models.

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering~(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning~(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Collaborative filtering based recommendation learns users’ preferences from all users’ historical behavior data, and has been popular to facilitate decision making. Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users’ sensitive attributes (e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. Given each real original user-item interaction record, we propose the following hypotheses for augmenting the training data: each user in one group has a similar item preference (click or non-click) as the item preference of any user in the remaining group. With these hypotheses, we generate “fake" interaction behaviors to complement the original training data. After that, we design a bi-level optimization target, with the inner optimization generates better fake data to augment training data with our hypotheses, and the outer one updates the recommendation model parameters based on the augmented training data. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.

Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced triples can be converted into various crowdsourced relationships, the available related methods are not effective in capturing these relationships to alleviate the harm to inference that is caused by conflicting answers. In this research, we propose aReliability-drivenMulti-viewGraphEmbedding framework forTruthinference (TiReMGE), which explores multiple crowdsourced relationships by organically integrating worker reliabilities into a graph space that is constructed from crowdsourced triples. Specifically, to create an interactive environment, we propose a reliability-driven initialization criterion for initializing vectors of tasks and workers as interactive carriers of reliabilities. From the perspective of multiple crowdsourced relationships, a multi-view graph embedding framework is proposed for reliability information interaction on a task-worker graph, which encodes latent crowdsourced relationships into vectors of workers and tasks for reliability update and truth inference. A heritable reliability updating method based on the Lagrange multiplier method is proposed to obtain reliabilities that match the quality of workers for interaction by a novel constraint law. Our ultimate goal is to minimize the Euclidean distance between the encoded task vector and the answer that is provided by a worker with high reliability. Extensive experimental results on nine real-world datasets demonstrate that TiReMGE significantly outperforms the nine state-of-the-art baselines.

Crowdsourcing platforms collect massive dirty claims that are provided by sources for crowdsourced objects, which prompts truth inference to be proposed for crowdsourcing data denoising. Although current graph-based truth-inference methods achieve remarkable success by capturing complex crowdsourcing relationships, they typically suffer from two challenges: 1) They fail to obtain complete crowdsourcing relationships because of the structural limitations of crowdsourcing relationship graphs; 2) Their vector initialization methods for objects and sources are disturbed by claim noise, which limits them from obtaining correct object and source semantics. To cope with these challenges, we propose a novelTruth-Inference method viaReliabilityAggregation (TIRA) on an object-source graph. Specifically, we propose a hierarchical graph auto-encoder to adapt to a reasonable object-source graph, which enables TIRA to capture complete crowdsourcing relationships from multiple perspectives. To better guide TIRA, we design a vector initialization method based on source reliabilities to map the denoised claims to a representation space of objects and sources. Finally, TIRA aggregates the reliability information on an object-source graph to generate object embeddings for truth inference. We conducted extensive experiments on 12 real-world datasets. The experimental results demonstrate that our method significantly outperforms 12 state-of-the-art baselines in terms of the$accuracy$and$weighted\_{F}1$.

In most E-commerce platforms, whether the displayed items trigger the user’s interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM) . That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, m ultimodal e ntity g raph c ollaborative f iltering, short for MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals. Finally, the sentiment information from the review data are used to fine-grainedly weight neighbor aggregation in the GCN, as it reflects the overall quality of the items, and therefore it is an important modality information related to user preferences. Extensive experiments demonstrate the effectiveness and rationality of MEGCF. 1

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

Text image captioning aims to understand the scene text in images for image caption generation. The key issue of this challenging task is to understand the relationship between the text OCR tokens and images. In this paper, we propose a novel text image captioning method by purifying the OCR-oriented scene graph with themaster object. The master object is the object to which the OCR is attached, which is the semantic relationship bridge between the OCR token and the image. We consider the master object as a proxy to connect OCR tokens and other regions in the image. By exploring the master object for each OCR token, we build the purified scene graph based on the master objects and then enrich the visual embedding by the Graph Convolution Network (GCN). Furthermore, we cluster the OCR tokens and feed the hierarchical information to provide a richer representation. Experiments on the TextCaps validation and test dataset demonstrate the effectiveness of the proposed method.

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.

Social recommendation has emerged to leverage social connections among users for predicting users’ unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user’s first-order social neighbors’ interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influenceDiffusionNetwork (i.e., DiffNet) for social recommendation L. Wu, P. Sun, Y. Fu, R. Hong, X. Wang, and M. Wang, “A neural influence diffusion model for social recommendation,” inProc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2019, pp. 235–244.. DiffNet models the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured in the user embedding process. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, in this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. This is achieved by iteratively aggregating each user’s embedding from three aspects: the user’s previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on four real-world datasets clearly show the effectiveness of our proposed model. We release the source code athttps://github.com/PeiJieSun/diffnet.

Scene text recognition has been widely investigated in computer vision. In the literature, the encoder-decoder based framework, which first encodes image into feature map and then decodes them into corresponding text sequences, have achieved great success. However, this solution fails in low-quality images, as the local visual features extracted from curved or blurred images are difficult to decode into corresponding text. To address this issue, we propose a new framework for Scene Text Recognition (STR), named Non-Local Attentional Scene Text Recognizer (NASTER). We use ResNet with Global Context Block (GC block) to extract global visual features. The global context information is then captured in parallel using the self-attention module and finally decoded by a multi-layer attention decoder with an intermediate supervision module. The proposed method achieves the state-of-the-art performances on seven benchmark datasets, demonstrating the effectiveness of our approach.

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard'' unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively.

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors are treated as unlinked edges in the user-item bipartite graph. As users' unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links. In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa ~(i.e., newly updated graph structure are optimized based on current node embedding results). Some previous works provided approaches to refine the graph structure. However, most of these graph learning models relied on node features for modeling, which are not available in CF. Besides, nearly all optimization goals tried to compare the learned adaptive graph and the original graph from a local reconstruction perspective, whether the global properties of the adaptive graph structure are modeled in the learning process is still unknown. To this end, in this paper, we propose an enhanced graph learning network EGLN approach for CF via mutual information maximization. The key idea of EGLN is two folds: First, we let the enhanced graph learning module and the node embedding module iteratively learn from each other without any feature input. Second, we design a local-global consistency optimization function to capture the global properties in the enhanced graph learning process. Finally, extensive experimental results on three real-world datasets clearly show the effectiveness of our proposed model.

As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently, researchers paid considerable attention to fairness issues for artificial intelligence applications. Most of these approaches assumed independence of instances, and designed sophisticated models to eliminate the sensitive information to facilitate fairness. However, recommender systems differ greatly from these approaches as users and items naturally form a user-item bipartite graph, and are collaboratively correlated in the graph structure. In this paper, we propose a novel graph based technique for ensuring fairness of any recommendation models. Here, the fairness requirements refer to not exposing sensitive feature set in the user modeling process. Specifically, given the original embeddings from any recommendation models, we learn a composition of filters that transform each user’s and each item’s original embeddings into a filtered embedding space based on the sensitive feature set. For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user. Finally, extensive experimental results clearly show the effectiveness of our proposed model for fair recommendation. We publish the source code at https://github.com/newlei/FairGo.

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.

Image dehazing is a fundamental task for the computer vision and multimedia and usually in the face of the challenge from two aspects, i) the uneven distribution of arbitrary haze and ii) the distortion of image pixels caused by the hazed image. In this paper, we propose an end-to-end trainable framework, named Weighted-Fusion Network with Poly-Scale Convolution (WFN-PSC), to address these dehazing issues. The proposed method is designed based on the Poly-Scale Convolution (PSConv). It can extract the image feature from different scales without upsampling and downsampled, which avoids the image distortion. Beyond this, we design the spatial and channel weighted-fusion modules to make the WFN-PSC model focus on the hard dehazing parts of image from two dimensions. Specifically, we design three Part Architectures followed by the channel weighted-fusion module. Each Part Architecture consists of three PSConv residual blocks and a spatial weighted-fusion module. The experiments on the benchmark demonstrate the dehazing effectiveness of the proposed method. Furthermore, considering that image dehazing is a low-level task in the computer vision, we evaluate the dehazed image on the object detection task and the results show that the proposed method can be a good pre-processing to assist the high-level computer vision task.

With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad applications. As users' visual preferences are subjective and vary from person to person, previous generalized video highlight extraction models fail to tailor to users' unique preferences. In this paper, we study the problem of personalized video highlight recommendation with rich visual content. By dividing each video into non-overlapping segments, we formulate the problem as a personalized segment recommendation task with many new segments in the test stage. The key challenges of this problem lie in: the cold-start users with limited video highlight records in the training data and new segments without any user ratings at the test stage. To tackle these challenges, an intuitive idea is to formulate a user-item interaction graph and perform inductive graph neural network based models for better user and item embedding learning. However, the graph embedding models fail to generalize to unseen items as these models rely on the item content feature and item link information for item embedding calculation. To this end, we propose an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec). TransGRec is composed of two parts: a graph neural network followed by an item embedding transfer network. Specifically, the graph neural network part exploits the higher-order proximity between users and segments to alleviate the user cold-start problem. The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase. We design two detailed implementations of the transfer learning optimization function, and we show how the two parts of TransGRec can be efficiently optimized with different transfer learning optimization functions. Please note that, our proposed framework is generally applicable to any inductive graph based recommendation model to address the new node problem without any link structure. Finally, extensive experimental results on a real-world dataset clearly show the effectiveness of our proposed model.

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user(item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user~(item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks.

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.

In many recommender systems, users express item opinions through two kinds of behaviors: giving preferences and writing detailed reviews. As both kinds of behaviors reflect users’ assessment of items, review enhanced recommender systems leverage these two kinds of user behaviors to boost recommendation performance. On the one hand, researchers proposed to better model the user and item embeddings with additional review information for enhancing preference prediction accuracy. On the other hand, some recent works focused on automatically generating item reviews for recommendation explanations with related user and item embeddings. We argue that, while the task of preference prediction with the accuracy goal is well recognized in the community, the task of generating reviews for explainable recommendation is also important to gain user trust and increase conversion rate. Some preliminary attempts have considered jointly modeling these two tasks, with the user and item embeddings are shared. These studies empirically showed that these two tasks are correlated, and jointly modeling them would benefit the performance of both tasks.

Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users’ two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many neural-based models have been designed to predict a single users’ behavior, i.e., social link behavior or consumption behavior. Compared to the classical shallow models, these neural-based models show better performance to drive a user’s behavior by modeling the complex patterns. However, there are few works exploiting whether it is possible to design a neural-based model to jointly predict users’ two kinds of behaviors to further enhance the prediction performance. In fact, social scientists have already shown that users’ two kinds of behaviors are not isolated; people trend to the consumption recommendation of friends on social platforms and would like to make new friends with like-minded users. While some previous works jointly model users’ two kinds of behaviors with shallow models, we argue that the correlation between users’ two kinds of behaviors are complex, which could not be well-designed with shallow linear models. To this end, in this article, we propose a neural joint behavior prediction model named Neural Joint Behavior Prediction Model (NJBP) to mutually enhance the prediction performance of these two tasks on social networking platforms. Specifically, there are two key characteristics of our proposed model: First, to model the correlation of users’ two kinds of behaviors, we design a fusion layer in the neural network to model the positive correlation of users’ two kinds of behaviors. Second, as the observed links in the social network are often very sparse, we design a new link-based loss function that could preserve the social network topology. After that, we design a joint optimization function to allow the two behaviors modeling tasks to be trained to mutually enhance each other. Finally, extensive experimental results on two real-world datasets show that our proposed method is on average 7.14% better than the best baseline on social link behavior while 6.21% on consumption behavior prediction. Compared with the pair-wise loss function on two datasets, our proposed link-based loss function improves at least 4.69% on the social link behavior prediction and 4.72% on the consumption behavior prediction.

Learning effective visual similarity is an essential problem in multimedia research. Despite the promising progress made in recent years, most existing approaches learn visual features and similarities in two separate stages, which inevitably limits their performance. Once useful information has been lost in the feature extraction stage, it can hardly be recovered later. This article proposes a novel end-to-end approach for visual similarity modeling, calleddeep neighborhood component analysis, which discriminatively trains deep neural networks to jointly learn visual features and similarities. Specifically, we first formulate a metric learning objective that maximizes the intra-class correlations and minimizes the inter-class correlations under the neighborhood component analysis criterion, and then train deep convolutional neural networks to learn a nonlinear mapping that projects visual instances from original feature space to a discriminative and neighborhood-structure-preserving embedding space, thus resulting in better performance. We conducted extensive evaluations on several widely used and challenging datasets, and the impressive results demonstrate the effectiveness of our proposed approach.

Sentiment analysis is an important topic concerning identification of feelings, attitudes, emotions and opinions from text. To automate such analysis, a large amount of example text needs to be manually annotated for model training. This is laborious and expensive, but the cross-domain technique is a key solution to reducing the cost by reusing annotated reviews across domains. However, its success largely relies on the learning of a robust common representation space across domains. In the recent years, significant effort has been invested to improve the cross-domain representation learning by designing increasingly more complex and elaborate model inputs and architectures. We support that it is not necessary to increase design complexity as this inevitably consumes more time in model training. Instead, we propose to explore the word polarity and occurrence information through a simple mapping and encode such information more accurately whilst managing lower computational costs. The proposed approach is unique and takes advantage of the stochastic embedding technique to tackle cross-domain sentiment alignment. Its effectiveness is benchmarked with over ten data tasks constructed from two review corpora and it is compared against ten classical and state-of-the-art methods.

Image based social networks are among the most popular social networking services in recent years. With a tremendous amount of images uploaded everyday, understanding users' preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information (e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users' preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model.

Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over de-rained or under de-rained result. To handle this issue, this paper proposes a new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet). To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i.e., Residual path and Dense path. The Residual path is used to reuse com-mon features from the previous layers while the Dense path can explore new features. In addition, to concatenate different scaled features, we also apply the idea of multi-stream with shortcuts between cascaded dual path residual dense block based streams. To obtain more distinct derained images, we combine the SSIM loss and perceptual loss to preserve the per-pixel similarity as well as preserving the global structures so that the deraining result is more accurate. Extensive experi-ments on both synthetic and real rainy images demonstrate that our MH-DerainNet can deliver significant improvements over several recent state-of-the-art methods.

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users, with these trusted users are influenced by the trusted users' social connections. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that the user has consumed, ICF recommends items that are similar to the user’s profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationships between items, which are insufficient to capture the complicated decision-making process of users. In this article, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationships among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. By doing this, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user’s profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

Due to the prevalence of group activities in people's daily life, recommending content to a group of users becomes an important task in many information systems. A fundamental problem in group recommendation is how to aggregate the preferences of group members to infer the decision of a group. Toward this end, we contribute a novel solution, namely AGREE (short for ''Attentive Group REcommEndation''), to address the preference aggregation problem by learning the aggregation strategy from data, which is based on the recent developments of attention network and neural collaborative filtering (NCF). Specifically, we adopt an attention mechanism to adapt the representation of a group, and learn the interaction between groups and items from data under the NCF framework. Moreover, since many group recommender systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, we can reinforce the two tasks of recommending items for both groups and users. By experimenting on two real-world datasets, we demonstrate that our AGREE model not only improves the group recommendation performance but also enhances the recommendation for users, especially for cold-start users that have no historical interactions individually.

As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users' adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users' complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users' product adoption rate over time, where various factors that may influence users' decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users' preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users' preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.

Many professional organizations produce regular reports of social indicators to monitor social progress. Despite their reasonable results and societal value, early efforts on social indicator computing suffer from three problems: 1) labor-intensive data gathering, 2) insufficient data, and 3) expert-relied data fusion. Towards this end, we present a novel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data. For each channel, this scheme presents the semi-structured and unstructured data with simple graphs and hypergraphs, respectively. It then groups the channels into different clusters according to their correlations. After that, it uses a unified model to learn the cluster-wise common spaces, perform ranking separately upon each space, and fuse these rankings to produce the final one. We take Chinese university ranking as a case study and validate our scheme over a real-world dataset. It is worth emphasizing that our scheme is applicable to computation of other social indicators, such as Educational attainment.

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users’ tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

Furniture style describes the discriminative appearance characteristics of furniture. It plays an important role in real-world indoor decoration. In this article, we explore the furniture style features and study the problem of furniture style classification. Differing from traditional object classification, furniture style classification aims at classifying different furniture in terms of the “style” that describes its appearance (e.g., American style, Gothic style, Rococo style, etc.) rather than the “kind” that is more related to its functional structure (e.g., bed, desk, etc.). To pursue efficient furniture style features, we construct a novel dataset of furniture styles that contains 16 common style categories and implement three strategies with respect to two categories of classification, that is, handcrafted classification and learning-based classification. First, we follow the typical image classification pipeline to extract the handcrafted features and train the classifier by support vector machine. Then we use the convolutional neural network to extract learning-based features from training images. To obtain comprehensive furniture style features, we finally combine the handcrafted image classification pipeline and the learning-based network. We experimentally evaluate the performances of handcrafted features and learning-based features of each strategy, and the results show the superiority of learning-based features and also the comprehensiveness of handcrafted features.

Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large and growing body of literature has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin, and photo posts from Instagram. On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on a real-world dataset thoroughly demonstrated the effectiveness of our proposed model.

Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data. In this paper, we propose a unique perspective to achieve this goal, which is quantifying user vitality by analyzing the dynamic interactions among users on social networks. Examples of social network include but are not limited to social networks in microblog sites and academical collaboration networks. Intuitively, if a user has many interactions with his friends within a time period and most of his friends do not have many interactions with their friends simultaneously, it is very likely that this user has high vitality. Based on this idea, we develop quantitative measurements for user vitality and propose our first algorithm for ranking users based vitality. Also, we further consider the mutual influence between users while computing the vitality measurements and propose the second ranking algorithm, which computes user vitality in an iterative way. Other than user vitality ranking, we also introduce a vitality prediction problem, which is also of great importance for many applications in social networking services. Along this line, we develop a customized prediction model to solve the vitality prediction problem. To evaluate the performance of our algorithms, we collect two dynamic social network data sets. The experimental results with both data sets clearly demonstrate the advantage of our ranking and prediction methods.

Sociologists have long converged that the evolution of a Social Networking Service(SNS) is driven by the interplay between users' preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users' historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users' two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users' behaviors: a direct (latent) representation that presumes users' behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users' two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion.

The emergence of Location-based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point-of-Interest (POI) recommender systems. Although a personalized POI recommender system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model user's POI decision making process and the difficulty to address data sparsity and user/location cold-start problem. To cope with these challenges, we define three types of friends (i.e., social friends, location friends, and neighboring friends) in LBSN, and develop a two-step framework to leverage the information of friends to improve POI recommendation accuracy and address cold-start problem. Specifically, we first propose to learn a set of potential locations that each individual's friends have checked-in before and this individual is most interested in. Then we incorporate three types of check-ins (i.e., observed check-ins, potential check-ins and other unobserved check-ins) into matrix factorization model using two different loss functions (i.e., the square error based loss and the ranking error based loss). To evaluate the proposed model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results demonstrate the effectiveness of our methods.

With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly help users to find their preferred POIs and assist POI owners to attract more customers. However, due to the complexity of users' checkin decision making process that is influenced by many different factors such as POI distance and region's prosperity, and the dynamics of user's preference, POI recommender systems usually suffer from many challenges. Although different latent factor based methods (e.g., probabilistic matrix factorization) have been proposed, most of them do not successfully incorporate both geographical influence and temporal effect together into latent factor models. To this end, in this paper, we propose a new Spatial-Temporal Probabilistic Matrix Factorization (STPMF) model that models a user's preference for POI as the combination of his geographical preference and other general interest in POI. Furthermore, in addition to static general interest of user, we capture the temporal dynamics of user's interest as well by modeling checkin data in a unique way. To evaluate the proposed STPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results clearly demonstrate the effectiveness of our proposed STPMF model.

With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-Of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly assist users to find their preferred POIs and help POI owners to attract more customers. However, it is very challenging to develop a personalized POI recommender system because a user's checkin decision making process is very complex and could be influenced by many factors such as social network and geographical distance. In the literature, a variety of methods have been proposed to tackle this problem. Most of these methods model user's preference for POIs with integrated approaches and consider all candidate POIs as a whole space. However, by carefully examining a longitudinal real-world checkin data, we find that the whole space of users' checkins actually consists of two parts: social friend space and user interest space. The social friend space denotes the set of POI candidates that users' friends have checked-in before and the user interest space refers to the set of POI candidates that are similar to users' historical checkins, but are not visited by their friends yet. Along this line, we develop separate models for the both spaces to recommend POIs. Specifically, in social friend space, we assume users would repeat their friends' historical POIs due to the preference propagation through social networks, and propose a new Social Friend Probabilistic Matrix Factorization (SFPMF) model. In user interest space, we propose a new User Interest Probabilistic Matrix Factorization (UIPMF) model to capture the correlations between a new POI and one user's historical POIs. To evaluate the proposed models, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on the real-world data set. The experimental results firmly demonstrate the effectiveness of our proposed models.

A large number of online reviews have been accumulated on the Web, such as Amazon.com and Cnet.com. It is increasingly challenging to digest these reviews for both consumers and firms as the volume of reviews increases. A promising direction to ease such a burden is to automatically identify aspects of a product and reveal each individual's ratings on them from these reviews. The identified and rated aspects can help consumers understand the pros and cons of a product and make their purchase decisions, and help firms learn user feedbacks and improve their products and marketing strategy. While different methods have been introduced to tackle this problem in the past, few of them successfully model the intrinsic connection between aspect and aspect rating particularly in short reviews. To this end, in this paper, we first propose the Aspect Identification and Rating (AIR) model to model observed textual reviews and overall ratings in a generative way, where the sampled aspect rating influences the sampling of sentimental words on this aspect. Furthermore, we enhance AIR model to particularly address one unique characteristic of short reviews that aspects mentioned in reviews may be quite unbalanced, and develop another model namely AIRS. Within AIRS model, we allow an aspect to directly affect the sampling of a latent rating on this aspect in order to capture the mutual influence between aspect and aspect rating through the whole generative process. Finally, we examine our two models and compare them with other methods based on multiple real world data sets, including hotel reviews, beer reviews and app reviews. Experimental results clearly demonstrate the effectiveness and improvement of our models. Other potential applications driven by our results are also shown in the experiments.

In this paper, we demonstrate that the essentials of image classification and retrieval are the same, since both tasks could be tackled by measuring the similarity between images. To this end, we propose ONE (Online Nearest-neighbor Estimation), a unified algorithm for both image classification and retrieval. ONE is surprisingly simple, which only involves manual object definition, regional description and nearest-neighbor search. We take advantage of PCA and PQ approximation and GPU parallelization to scale our algorithm up to large-scale image search. Experimental results verify that ONE achieves state-of-the-art accuracy in a wide range of image classification and retrieval benchmarks.

10.1145/2732265

In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel k NN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise one-vs- k NN sparse reconstructions of all samples can remove most of the semantically unrelated links among the data, and thus it is more robust and discriminative than the conventional graphs. Meanwhile, we apply the approximate k nearest neighbors to accelerate the sparse graph construction without loosing its effectiveness. More importantly, we propose an effective training label refinement strategy within this graph-based learning framework to handle the noise in the training labels, by bringing in a dual regularization for both the quantity and sparsity of the noise. We conduct extensive experiments on a real-world image database consisting of 55,615 Flickr images and noisily tagged training labels. The results demonstrate both the effectiveness and efficiency of the proposed approach and its capability to deal with the noise in the training labels.