Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or feedback, which limits their ability to prevent unreliable responses in advance. In this paper, we introduce Aspect-Based Causal Abstention (ABCA), a new framework that enables early abstention by analysing the internal diversity of LLM knowledge through causal inference. This diversity reflects the multifaceted nature of parametric knowledge acquired from various sources, representing diverse aspects such as disciplines, legal contexts, or temporal frames. ABCA estimates causal effects conditioned on these aspects to assess the reliability of knowledge relevant to a given query. Based on these estimates, we enable two types of abstention: Type-1, where aspect effects are inconsistent (knowledge conflict), and Type-2, where aspect effects consistently support abstention (knowledge insufficiency). Experiments on standard benchmarks demonstrate that ABCA improves abstention reliability, achieves state-of-the-art performance, and enhances the interpretability of abstention decisions.

Online conversations have become more prevalent on public discussion platforms (e.g. Reddit). With growing controversial topics, it is desirable to summarize not only diverse arguments, but also their rationale and justification. Early studies on text summarization focus on capturing general salient information in source documents, overlooking the argumentative nature of online conversations. Recent research on conversation summarization although considers the argumentative relationship among sentences, fail to explicate deeper argument structure within sentences for summarization. In this paper, we propose a novel task of argument-aware quantitative summarization to reveal the claim-reason structure of arguments in conversations, with quantities measuring argument strength. We further propose ARQUSUMM, a novel framework to address the task. To reveal the underlying argument structure within sentences, ARQUSUMM leverages LLM few-shot learning grounded in the argumentation theory to identify propositions within sentences and their claim-reason relationships. For quantitative summarization, ARQUSUMM employs argument structure-aware clustering algorithms to aggregate arguments and quantify their support. Experiments show that ARQUSUMM outperforms existing conversation and quantitative summarization models and generate summaries representing argument structures that are more helpful to users, of high textual quality and quantification accuracy.

Cross-domain fake news detection (CD-FND) transfers knowledge from a source domain to a target domain and is crucial for real-world fake news mitigation. This task becomes particularly important yet more challenging when the target domain is previously unseen (e.g., the COVID-19 outbreak or the Russia-Ukraine war). However, existing CD-FND methods overlook such scenarios and consequently suffer from the following two key limitations: (1) insufficient modeling of high-level semantics in news and user engagements; and (2) scarcity of labeled data in unseen domains. Targeting these limitations, we find that large language models (LLMs) offer strong potential for CD-FND on unseen domains, yet their effective use remains non-trivial. Nevertheless, two key challenges arise: (1) how to capture high-level semantics from both news content and user engagements using LLMs; and (2) how to make LLM-generated features more reliable and transferable for CD-FND on unseen domains. To tackle these challenges, we propose DAUD, a novel LLM-based Domain-Aware framework for fake news detection on Unseen Domains. DAUD employs LLMs to extract high-level semantics from news content. It models users' single- and cross-domain engagements to generate domain-aware behavioral representations. In addition, DAUD captures the relations between original data-driven features and LLM-derived features of news, users, and user engagements. This allows it to extract more reliable domain-shared representations that improve knowledge transfer to unseen domains. Extensive experiments on real-world datasets demonstrate that DAUD outperforms state-of-the-art baselines in both general and unseen-domain CD-FND settings.

The rising prevalence of vision-threatening eye diseases poses a major global health and economic burden, yet timely diagnosis remains limited by workforce shortages, diagnostic delays, and restricted access to specialized care. Artificial intelligence (AI) offers potential solutions. In particular, recent progress in foundation models and large language models-especially multimodal large language models (MLLMs)-has shown promise in medical image interpretation and automated clinical documentation. However, advancing MLLMs for ophthalmology is hindered by the lack of unified, comprehensive benchmark datasets for development and evaluation. Most existing benchmarks were designed for earlier models, which focused on narrow tasks or specific disease conditions. These benchmarks typically provide outputs in the form of disease labels rather than free-text responses. As a result, they are less suitable for assessing emerging generative models. In this work, we present LMOD+, a large-scale multimodal ophthalmology benchmark dataset comprising 32,633 instances with multi-granular annotations across 12 common ophthalmic conditions and 5 imaging modalities. The dataset integrates imaging, anatomical structures, demographics, and free-text annotations. It supports primary ophthalmic applications such as anatomical structure recognition, disease screening, disease staging, and demographic prediction for potential performance bias evaluation. Alongside the dataset, we introduce a systematic and unified data curation pipeline that repurposes existing or new datasets for MLLM development. LMOD+ extends our preliminary LMOD benchmark-the first multimodal ophthalmology benchmark for MLLMs-with three major enhancements. First, we expanded the dataset by nearly 50% (from 21,933 to 32,633 instances). The color fundus photography (CFP) modality, the most accessible imaging modality in ophthalmology, was significantly enlarged to cover a broader range of pathological conditions. Second, we broadened task coverage to include (a) 12 binary disease diagnosis tasks for prevalent conditions such as diabetic retinopathy, age-related macular degeneration, and retinal vein occlusion; (b) multi-class ophthalmic disease diagnosis; (c) disease severity classification, including a diabetic retinopathy staging task, which uses two internationally adopted grading standards: the international clinical diabetic retinopathy classification and the Scottish diabetic retinopathy grading scheme classification; and (d) demographic prediction (age and sex) to assess potential model bias. Third, we systematically evaluated 24 state-of-the-art MLLMs, including recent models from the InternVL, Qwen, and DeepSeek families. Our evaluations highlight both the promise and limitations of current MLLMs in ophthalmology. For example, Qwen-7B and InternVL achieved accuracies of 58.26% and 57.83% in disease screening under a zero-shot setting with a single model-a considerably more challenging paradigm than traditional fine-tuning, where separate models are trained for each specific task. InternVL also demonstrated potential in anatomical recognition. Nonetheless, overall performance remained suboptimal and often close to random baselines for challenging tasks such as disease staging, underscoring the substantial gap between general-domain MLLMs and the specialized requirements of ophthalmology. We publicly release the dataset, curation pipeline, and leaderboard to encourage community-wide development and evaluation of MLLMs, with the goal of advancing ophthalmic applications and ultimately reducing the global burden of vision-threatening diseases through AI. The dataset website, benchmark leaderboard, and download link are available at https://kfzyqin.github.io/lmod_plus.

Caching web data on edge servers has become a common practice in latency-sensitive services to minimize data retrieval delays for web users. However, the geographic distribution of edge servers and frequent data transmissions make these systems vulnerable to security threats, particularly cache pollution attacks (CPAs). In such attacks, malicious users send excessive requests for unpopular data at abnormal frequencies, causing irrelevant content to be cached and degrading the system’s performance. Traditional CPAs, though impactful in conventional caching systems, are less effective in edge environments where user requests are more diverse and edge servers collaborate in caching strategies. In this paper, we identify a novel attack named data flipping attack (DFA) that targets the data transmission process among edge servers. This attack manipulates request distribution by swapping the frequencies of popular and unpopular data requests, all while maintaining other characteristics like request timing and user identity. This tactic disrupts caching strategies without raising suspicion. Experimental results indicate DFA is independent of user request patterns and demonstrates substantial effectiveness and robustness, successfully forcing edge web users to retrieve data from the cloud across various scales and configurations of edge networks. Furthermore, it evades detection by state-of-the-art methods that rely on specific distribution patterns, such as the Zipf distribution. To counter this attack, we propose an effective defense method that alters the request distribution by frequency distillation, mitigating its impact.

Graphs are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottlenecks due to the inherent irregularity of graphs. An innovative solution is converting graphs into textual representations, thereby harnessing the powerful capabilities of Large Language Models (LLMs) to process and comprehend graphs. In this article, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph. The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze. Thus, we propose a novel taxonomy of LLM4graph methods from the view of the transformation. Specifically, existing methods can be divided into two paradigms: Graph2text and Graph2token, which transform graphs into texts or tokens as the input of LLMs, respectively. We point out four challenges during the transformation to systematically present existing methods from a problem-oriented perspective. For practical concerns, we provide a guideline for researchers on selecting appropriate models and LLMs for different graphs and hardware constraints. To empirically evaluate our taxonomy and different technical choices, we conduct experiments with representative methods in Graph2text and Graph2token. We also identify five future research directions for LLM4graph.

Review-based Product Question Answering (PQA) allows e-commerce platforms to automatically address customer queries by leveraging insights from user reviews.However, existing PQA systems generate answers with only a single perspective, failing to capture the diversity of customer opinions.In this paper we introduce a novel task Quantitative Query-Focused Summarization (QQSUM), which aims to summarize diverse customer opinions into representative Key Points (KPs) and quantify their prevalence to effectively answer user queries.While Retrieval-Augmented Generation (RAG) shows promise for PQA, its generated answers still fall short of capturing the full diversity of viewpoints.To tackle this challenge, our model QQSUM-RAG, which extends RAG, employs few-shot learning to jointly train a KP-oriented retriever and a KP summary generator, enabling KP-based summaries that capture diverse and representative opinions.Experimental results demonstrate that QQSUM-RAG achieves superior performance compared to state-of-the-art RAG baselines in both textual quality and quantification accuracy of opinions.Our source code is available at: https://github.com/ antangrocket1312/

Senqi Yang, Dongyu Zhang, Jing Ren, Ziqi Xu, Xiuzhen Zhang, Yiliao Song, Hongfei Lin, Feng Xia. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Large Language Models (LLMs) have shown impressive capabilities in natural language processing but still struggle to perform well on knowledge-intensive tasks that require deep reasoning and the integration of external knowledge. Although methods such as Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) have been proposed to enhance LLMs with external knowledge, they still suffer from internal bias in LLMs, which often leads to incorrect answers. In this paper, we propose a novel causal prompting framework, Conditional Front-Door Prompting (CFD-Prompting), which enables the unbiased estimation of the causal effect between the query and the answer, conditional on external knowledge, while mitigating internal bias. By constructing counterfactual external knowledge, our framework simulates how the query behaves under varying contexts, addressing the challenge that the query is fixed and is not amenable to direct causal intervention. Compared to the standard front-door adjustment, the conditional variant operates under weaker assumptions, enhancing both robustness and generalisability of the reasoning process. Extensive experiments across multiple LLMs and benchmark datasets demonstrate that CFD-Prompting significantly outperforms existing baselines in both accuracy and robustness.

Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustworthiness of outputs from these models. This study focuses on Explanation for MTPP, aiming to identify the minimal and rational explanation, that is, the minimum subset of events in history, based on which the prediction accuracy of MTPP matches that based on full history to a great extent and better than that based on the complement of the subset. This study finds that directly defining Explanation for MTPP as counterfactual explanation or factual explanation can result in irrational explanations. To address this issue, we define Explanation for MTPP as a combination of counterfactual explanation and factual explanation. This study proposes Counterfactual and Factual Explainer for MTPP (CFF) to solve Explanation for MTPP with a series of deliberately designed techniques. Experiments demonstrate the correctness and superiority of CFF over baselines regarding explanation quality and processing efficiency.

Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance.However, the effect of pruning on the fairness of LLMgenerated summaries remains unexplored, particularly for opinion summarisation where biased outputs could influence public views.In this paper, we present a comprehensive empirical analysis of opinion summarisation, examining three state-of-the-art pruning methods and various calibration sets across three opensource LLMs using four fairness metrics.Our systematic analysis reveals that pruning methods have a greater impact on fairness than calibration sets.Building on these insights, we propose High Gradient Low Activation (HGLA) pruning, which identifies and removes parameters that are redundant for input processing but influential in output generation.Our experiments demonstrate that HGLA can better maintain or even improve fairness compared to existing methods, showing promise across models and tasks where traditional methods have limitations.Our human evaluation shows HGLA-generated outputs are fairer than existing state-of-the-art pruning methods.

Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Language Models (LLMs) offer promise in automating counter-response generation to mitigate misinformation, but a critical challenge lies in their tendency to hallucinate non-factual information. Existing models mainly rely on LLM self-feedback to reduce hallucination, but this approach is computationally expensive. In this paper, we propose MisMitiFact, Misinformation Mitigation grounded in Facts, an efficient framework for generating fact-grounded counter-responses at scale. MisMitiFact generates simple critique feedback to refine LLM outputs, ensuring responses are grounded in evidence. We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites to identify and correct errors in key elements such as numerals, entities, and topics in LLM generations. Experiments show that MisMitiFact generates counter-responses of comparable quality to LLMs' self-feedback while using significantly smaller critique models. Importantly, it achieves ~5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation. Code and additional results are available at https://github.com/xxfwin/MisMitiFact.

Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with some marks being frequent but others rare. The imbalance poses a significant challenge to the performance of the next event prediction, especially for events of rare marks. To address this issue, we propose a thresholding method, which learns thresholds to tune the mark probability normalized by the mark's prior probability to optimize mark prediction, rather than predicting the mark directly based on the mark probability as in existing studies. In conjunction with this method, we predict the mark first and then the time. In particular, we develop a novel neural Marked Temporal Point Process (MTPP) model to support effective time sampling and estimation of mark probability without computationally expensive numerical improper integration. Extensive experiments on real-world datasets demonstrate the superior performance of our solution against various baselines for the next event mark and time prediction. The code is available at https://github.com/undes1red/IFNMTPP.

Spiking Neural Networks (SNNs) have attracted significant attention from the research community due to their high energy efficiency compared to Artificial Neural Networks (ANNs). However, rare studies on the security of SNNs were conducted, especially in backdoor attacks. Existing defense methods for ANN backdoor attacks either perform poorly or can be easily bypassed in SNN scenarios due to SNNs’ event-driven and temporal dependency characteristics, posing significant research challenges. In this paper, we identify the blockers to existing backdoor defenses for defending against attacks in SNNs and propose an unsupervised post-training backdoor detection method named Temporal Membrane Potential Backdoor Detection (TMPBD) to address those blockers in SNNs with neuromorphic data. Specifically, TMPBD employs the maximum margin statistic of temporal membrane potential in the last spiking layer of the SNNs to detect attack target labels without knowledge of the attack or access to any data. Moreover, we also design a practical and robust mitigation mechanism named Neural Dendrites Suppression Backdoor Mitigation (NDSBM). NDSBM dually clamps the neural dendrites, i.e., the weights connecting the first two convolution layers in each convolution block to limit the backdoor effect, while preserving the benign model behaviors learned from the temporal membrane potential obtained from a small, clean, unlabeled dataset in the same domain. To evaluate the performance, we conduct a comprehensive evaluation with multiple backdoor attack techniques, including the SOTA input-aware dynamic trigger attack dedicated to SNNs with clean models on three neuromorphic benchmark datasets. The results demonstrated that TMPBD achieves 100% prediction accuracy in detecting dynamic trigger attacks and associating attack target labels in all benchmark datasets. NDSBM lowered the attack success rate (ASR) from 100% caused by the dynamic trigger attack down to 8.44% with only mitigation or 2.81% when combined with detection for an end-to-end pipeline without performance degradation in clean accuracy.

Cross-domain fake news detection aims to mitigate domain shift and improve detection performance by transferring knowledge across domains. Existing approaches transfer knowledge based on news content and user engagements from a source domain to a target domain. However, these approaches face two main limitations, hindering effective knowledge transfer and optimal fake news detection performance. Firstly, from a micro perspective, they neglect the negative impact of veracity-irrelevant features in news content when transferring domain-shared features across domains. Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. Firstly, we propose a micro-hierarchical disentangling module to disentangle veracity-relevant and veracity-irrelevant features from news content in the source domain for improving fake news detection performance in the target domain. Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms the state-of-the-art baselines.

Itinerary recommendation involves suggesting a sequence of Points of Interests (POIs) that users obtain maximum satisfaction under a time budget. Existing models have three challenges. First, they model user interest as non-time dependent, which cannot capture user interest appropriately because user interest can be contextual on time, e.g., interest in restaurants are likely higher during typical meal times. Second, they model the distance dependency of user interest as a linear one, which does not always adequately capture this relationship, e.g., it could be a cubic decay relationship. Finally, existing studies treat POI recommendation and itinerary optimisation as two separate problems, which can result in sub-optimal itinerary recommendations. In this paper, we propose a deep learning model that recommends POIs and constructs the itinerary simultaneously and in an integrated manner. It captures user dynamic interest and non-linear spatial dependencies in itinerary recommendations. The proposed model has two steps, where the candidate selection policy generates a set of personalised candidate POIs based on user interest and the itinerary construction step maximises user interest within budget time. To recommend an appropriate candidate set, we propose a multi-head, attention-based transformer to leverage periodic trends and recent activities to capture user dynamic preferences. We also introduce a new co-visiting patterns-based graph convolutional network (GCN) model to capture user non-linear spatial dependencies. To construct the full itinerary from the dynamic candidate sets, we apply greedy policy that incrementally constructs itineraries within the budget time which aims to maximise user interest and minimize queuing time. Experimental results show that the proposed deep learning model outperforms state-of-the-art baselines in itinerary recommendation in four theme parks and four cities datasets. The proposed model outperforms the baselines in itinerary recommendation from 7.79% to 26.28% on various datasets in terms of F1-score value. We also show that the proposed candidate generation approach outperforms the state-of-the-art next POI recommendation models in eight real datasets. The proposed model outperforms the baselines on average by 11.29 % in terms of F1-score@5 values and 9.08% in terms of F1-score@10 values. We have publicly shared our source code at GitHub 1 for the reproducibility of our proposed model.

This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.

Key Point Analysis (KPA) aims for quantitative summarization that provides key points (KPs) as succinct textual summaries and quantities measuring their prevalence.KPA studies for arguments and reviews have been reported in the literature.A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence.Recent abstractive approaches still generate KPs based on sentences, often leading to KPs with overlapping and hallucinated opinions, and inaccurate quantification.In this paper, we propose Prompted Aspect Key Point Analysis (PAKPA) for quantitative review summarization.PAKPA employs aspect sentiment analysis and prompted in-context learning with Large Language Models (LLMs) to generate and quantify KPs grounded in aspects for business entities, which achieves faithful KPs with accurate quantification, and removes the need for large amounts of annotated data for supervised training.Experiments on the popular review dataset Yelp and the aspect-oriented review summarization dataset SPACE show that our framework achieves state-of-the-art performance.Source code and data are available at: https://github.com/ antangrocket1312/PAKPA Key Point Prevalence Matching Comments Friendly and helpful staff.46 From the minute I walked in the door the staff has treated me with such kindness and respect, there are not enough words I can say of how much gratitude I have for the staff here.It's a fine hotel, pretty basic, and all of the staff members we encountered were quite friendly.Excellent location for business travelers.37 Its a great location for business travelers since I can stay here and always be going the opposite way of traffic.*This hotel has an IDEAL location, the shuttle was perfect, and we got a great deal on priceline.Clean and comfortable rooms.32 *You will enjoy the breathtaking views from your spacious, clean and comfortable room.The rooms were nice and clean and the bed was comfortable.Convenient and helpful shuttle service.12 *This hotel has an IDEAL location, the shuttle was perfect, and we got a great deal on priceline.I would also like to give a shout out to the terrific shuttle drivers Jeff and Rod who were so great to my family group.Amazing view of Vanderbilt football stadium or The Parthenon.10 *You will enjoy the breathtaking views from your spacious, clean and comfortable room.Then the surprise came when we opened the curtains to see a full on view of the Vanderbilt football stadium.

Transfer of knowledge across domains is the focus for cross-domain and multi-domain fake news detection. However, most of the existing methods based on cross-domain knowledge transfer have two issues: (1) they usually ignore domain-specific features; (2) they are less effective in handling the imbalanced data distribution across domains. Targeting these two issues, we focus on how to effectively leverage user-news engagements in both data-richer and data-sparser domains. This is because not only users' engagement characteristics closely relate to the veracity of the engaged news, but also there are consistent patterns in common users' engagements with news across domains. Considering these two insights, this work aims to perform dual-target cross-domain fake news detection via well modeling users' engagement patterns. In particular, it aims to transfer knowledge based on user-news engagements for handling the imbalanced data distribution across domains, which is novel but challenging. To this end, in this paper, we propose a novel framework to mine User-news engagement Patterns for DuAl-TargEt cross-domain fake news detection (UPDATE). In UPDATE, we first mine user-news engagement patterns as the key auxiliary information for cross-domain knowledge transfer. In such a way, it avoids the necessity to remove the domain-specific news information, and thereby, better preserve useful news information. Then, we combine engagement features of common users in both data-richer and data-sparser domains. By doing so, UPDATE improves the information richness in each of the two domains, thus improving detection performance in both domains when detecting news from domains with imbalanced data distribution. Extensive experiments conducted on real-world datasets demonstrate that UPDATE significantly outperforms state-of-the-art cross-domain and multi-domain methods as well as large language models (LLMs), such as GPT-3.5-turbo in terms of AUC and Fl-score for fake news detection.

Opinion summarisation aims to summarise the salient information and opinions presented in documents such as product reviews, discussion forums, and social media texts into short summaries that enable users to effectively understand the opinions therein.Generating biased summaries has the risk of potentially swaying public opinion.Previous studies focused on studying bias in opinion summarisation using extractive models, but limited research has paid attention to abstractive summarisation models.In this study, using political bias as a case study, we first establish a methodology to quantify bias in abstractive models, then trace it from the pre-trained models to the task of summarising social media opinions using different models and adaptation methods.We find that most models exhibit intrinsic bias.Using a social media text summarisation dataset and contrasting various adaptation methods, we find that tuning a smaller number of parameters is less biased compared to standard fine-tuning; however, the diversity of topics in training data used for fine-tuning is critical.

In E-Commence applications, online shoppers increasingly rely on reviews to find answers for their queries on products. Recently, there has been significant research on product question answering using reviews. While many neural models have been proposed to generate answers from reviews to answer product questions, the evaluation of review-based product question answering (RPQA) models is still largely using lexicon-based text similarity metrics against reference answers. In this paper, we propose a distant supervised contrastive learning network based on modern transformer-based language models by leveraging distant ground-truth community question answers to train a model for RPQA evaluation. At deployment, the learned metric does not require reference answers to evaluate the generated answers for product queries. We conducted extensive experiments on publicly available AmazonQA datasets to evaluate RPQA models. Experiment results show that our learned metric correlates well with human judgements, and outperforms existing lexicon-based or embedding-based metrics.

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.

In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow, and the research must consider other important factors, particularly trustworthiness. A trustworthy recommender system (TRS) should not only be accurate but also transparent, unbiased, fair, and robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast-developing field of TRSs. To this end, in this article, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Sentiment analysis and opinion mining of the opinion-bearing text are important tasks in NLP.The Appraisal framework in systemic functional linguistics is a theory for analysing the linguistic patterns for expressing emotion and opinion.Manual annotation of appraisals however, requires linguistic expertise, and is costly and time-consuming.In this paper, we study how to automatically identify and tag appraisal text segments.We formulate the problem as a sequence tagging problem and propose a novel approach, Adaptive Appraisal (A 2 ), which employs task and sentiment adapters on pre-trained language models for sequence appraisal tagging.Experiments on user comments, blogs and microblogs show that A 2 outperforms baseline models and achieves good performance for cross-domain and crosslingual settings.Source code for A 2 is available at: https://github.com/ltian678/AA-code.git

State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g. the influence campaign by Russia’s Internet Research Agency on the 2016 US Election), and they fall short when dealing with novel campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce campaign-specific transformer adapters to MetaTroll to “memorise” campaign-specific knowledge so as to tackle catastrophic forgetting, where a model “forgets” how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git

Users’ purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, resulting in a basket of items. However, most of the existing works simply predict the next basket from a shallow perspective of (purchase) sequence data modeling without deep insight into the underlying factors which drive user purchase behaviors. In fact, filling a basket with multiple items is a process to incrementally satisfy a user's demand. Therefore, the key challenges to predict a user's next basket lie in (1) how to track the changes of the user's demand, and (2) how to satisfy her demand at a given moment. To this end, we propose an Evolving DEmand SAtisfaction (EvoDESA) model to model a user's demand evolution for next-basket prediction. In EvoDESA, a demand evolution module learns the dynamics of user demand over a sequence of basket-purchase behaviors. Then, a next-basket planning module effectively packs an optimal combination of items to best satisfy the user's current demand. Extensive experiments on three real-world transaction datasets demonstrate the considerable superiority of EvoDESA over the state-of-the-art approaches.

While detection of rumours in online social networks has been intensively studied in the literature, mitigation of the spread of rumours has only recently gained attention and remains a challenging task. Some studies developed user opinion models to find top influential users as debunkers to spread the truth to counter rumour spread. Other studies designed an intervention framework to optimize the mitigation activities for given debunkers. The issue of optimizing the selection of debunkers in a dynamic environment where users’ beliefs and behaviour change remains under investigated. This paper addresses this issue by proposing a rumour mitigation approach based on the deep reinforcement learning framework. In particular, we model the changes in users’ beliefs with an epidemic model. We further employ deep reinforcement learning to train an agent to learn a multi-stage policy for selecting the optimal debunkers to inject truthful information under a budget constraint. Our model selects debunkers to inject truthful information at multiple stages with an overall objective to maximize the number of users who will believe in the true information (a.k.a number of recovered nodes), such that the spread of rumours is minimized. Our experiments on synthetic and real-world social networks show that our proposed method for rumour mitigation can effectively minimize the spread of rumours.

With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have achieved a great success and benefted the society. However, most of the research on RS has focused on the improvement of the recommendation accuracy, while ignoring other important qualities, such as trustworthiness (robustness, fairness, explainability, privacy and security) and social impact (influence on users' recognition and behaviours) of the recommendations. These are important aspects and cannot be overlooked since they measure properties that determine whether the recommendation service is reliable, trustworthy and benefcial to individual users and society. In this work, responsible recommendations refer to trustworthy recommendation techniques and positive-social-impact recommendation results.

Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption.Motivated by the observation that the user network -which captures who engages with a storyand the comment network -which captures how they react to it -provide complementary signals for rumour detection.In this paper, we propose DUCK (rumour detection with user and comment networks) for rumour detection on social media.We study how to leverage transformers and graph attention networks to jointly model the contents and the structure of social media conversations, as well as the network of users who engage in these conversations.Over four widely used benchmark rumour datasets in English and Chinese, we show that DUCK produces superior performance for detecting rumours, creating a new state-of-the-art.

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.

Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selecting top k influential users on social networks as debunkers, but the social influence of debunkers may not translate to wide mitigation information propagation as expected. Other studies assume a given set of debunkers and focus on optimizing intensity for debunkers to publish true news, but as debunkers are fixed, even if with high social influence and/or high intensity to post true news, the true news may not reach users exposed to fake news and therefore mitigation effect may be limited. In this paper, we propose the multi-stage fake news mitigation campaign where debunkers are dynamically selected within budget at each stage. We formulate it as a reinforcement learning problem and propose a greedy algorithm optimized by predicting future states so that the debunkers can be selected in a way that maximizes the overall mitigation effect. We conducted extensive experiments on synthetic and real-world social networks and show that our solution outperforms state-of-the-art baselines in terms of mitigation effect.

Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are urgently needed. Most of the existing work on fake news mitigation focuses on the overall mitigation on a whole social network while ignoring developing concrete mitigation strategies to deter individual users from sharing fake news. In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. Our proposed model Rec4Mit (Recommendation for Mitigation) not only effectively captures a user’s current reading preference with a focus on which event, e.g., US election, from her/his recent reading history containing true and/or fake news, but also accurately predicts the veracity (true or fake) of candidate news. As a result, Rec4Mit can recommend the most suitable true news to best match the user’s preference as well as to mitigate fake news. In particular, for those users who have read fake news of a certain event, Rec4Mit is able to recommend the corresponding true news of the same event. Extensive experiments on real-world datasets show Rec4Mit significantly outperforms the state-of-the-art news recommendation methods in terms of the capability to recommend personalized true news for fake news mitigation.

Point-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor's enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub (https://github.com/sajalhalder/TLR-M_UI).

Abstract Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithmEffiTourRecusing an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.

Understanding the spread of rumors on online social networks (OSNs) is crucial for designing strategies to detect and mitigate rumor propagation. Previous studies analysing rumor propagation have focused on summarising the static measurements of propagation-based information cascades. But static features are unable to capture the dynamic nature of information propagation across time. In this paper, we employed two generative models, Multivariate Hawkes process (MHP) and marked Hawkes process (marked HP) to model user influence and the dynamics of rumor propagation. Using the MHP model, we were able to derive a novel measurement of user influence in information propagation, namely, the influence rate. We then employed the marked HP model and considered various mark measurements including the proposed influence rate to provide new insights into differentiating between rumor and non-rumor propagation, and among different types of rumor propagation. Our analysis on Twitter rumor datasets clearly showed that users play different roles (i.e., possess different influence rates) across different categories of source tweets. Moreover, different categories of source tweets have different patterns of diffusion. In particular, rumor cascades typically attracted more influential users at the early stage of cascades, and they are more likely to generate more retweets than non-rumor cascades. Among different types of rumors, false rumors diffused faster than true rumors.

Extractive-abstractive hybrid summarization can generate readable, concise summaries for long documents. Extraction-then-abstraction and extraction-with-abstraction are two representative approaches to hybrid summarization. But their general performance is yet to be evaluated by large scale experiments.We examined two state-of-the-art hybrid summarization algorithms from three novel perspectives: we applied them to a form of headline generation not previously tried, we evaluated the generalization of the algorithms by testing them both within and across news domains; and we compared the automatic assessment of the algorithms to human comparative judgments. It is found that an extraction-then-abstraction hybrid approach outperforms an extraction-with-abstraction approach, particularly for cross-domain headline generation.

Abstract The star‐rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the “true quality” of products and services is challenging. The mean‐rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the “helpfulness” social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on “helpfulness” achieve better performance than the statistical and heuristic baseline approaches.

With the increasing popularity of e-commerce, the number of product-related queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic question-answering systems can be immensely helpful. Product queries are, however, very different from open-domain questions: they tend to be product-specific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming state-of-the-art systems by consistently finding the most relevant reviews for product queries.

Min Peng, Qianqian Xie, Yanchun Zhang, Hua Wang, Xiuzhen Zhang, Jimin Huang, Gang Tian. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.

Deep learning-based visual sentiment analysis requires a large dataset for training. Dataset from social networks is popular but noisy because some images collected in this manner are mislabeled. Therefore, it is necessary to refine the dataset. Based on observations to such datasets, we propose a refinement algorithm based on the sentiments of adjective-noun pairs (ANPs) and tags. We first determine the unreliably labeled images through the sentiment contradiction between the ANPs and tags. These images are removed if the numbers of tags with positive and negative sentiments are equal. The remaining images are labeled again based on the majority vote of the tags' sentiments. Furthermore, we improve the traditional deep learning model by combining the softmax and Euclidean loss functions. Additionally, the improved model is trained using the refined dataset. Experiments demonstrate that both the dataset refinement algorithm and improved deep learning model are beneficial. The proposed algorithms outperform the benchmark results.

In this paper we propose a novel algorithm for mining contrast patterns using a distributed, map-reduce like framework. Contrast patterns describe differences between contrasted data sets and have previously been used for building highly accurate classifiers. However, mining for contrast patterns is a computationally expensive task and existing algorithms are designed to run in a sequential manner on a single machine. Consequently, existing approaches are unable to handle dense, high volume and high dimensional databases. Our algorithm addresses this problem by partitioning the search-space for contrast patterns into small, independent units. These units can be mined in parallel, providing a scalable solution for mining large data sets. Using three different real-world data sets we test an implementation of our algorithm on a Spark cluster. Results of these tests indicate that our algorithm achieves a high-degree of parallelism and scalability.

Many types of search tasks are answered through the computation of a ranked list of suggested answers. We re-examine the usual assumption that answer lists should be as long as possible, and suggest that when the number of matching items is potentially small -- perhaps even zero -- it may be more helpful to "quit while ahead", that is, to truncate the answer ranking earlier rather than later. To capture this effect, metrics are required which are attuned to the length of the ranking, and can handle cases in which there are no relevant documents. In this work we explore a generalized approach for representing truncated result sets, and propose modifications to a number of popular evaluation metrics.

Vehicle routing is known as the most challenging but an important problem in the transportation and logistics filed. The task is to optimise a set of vehicle routes to serve a group of customers with minimal delivery cost while respecting the problem constraints such as arriving within given time windows. This study presented a math-hyper-heuristic approach to tackle this problem more effectively and more efficiently. The proposed approach consists of two phases: a math phase and a hyper-heuristic phase. In the math phase, the problem is decomposed into sub-problems which are solved independently using the column generation algorithm. The solutions for these sub-problems are combined and then improved by the hyper-heuristic phase. Benchmark instances of large-scale vehicle routing problems with time windows were used for evaluation. The results show the effectiveness of the math phase. More importantly the proposed method achieved better solutions in comparison with two state of the art methods on all instances. The computational cost of the proposed method is also lower than that of other methods.

In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness of a seller without taking any transaction context information into account. With such a result as the indication of reputation, a buyer may be easily deceived by a malicious seller in a transaction where the notorious value imbalance problem is involved—in other words, a malicious seller accumulates a high-level reputation by selling cheap products and then deceives buyers by inducing them to purchase more expensive products. In this article, we first present a trust vector consisting of three values for contextual transaction trust (CTT). In the computation of CTT values, three identified important context dimensions , including Product Category, Transaction Amount, and Transaction Time, are taken into account. In the meantime, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters specified by a buyer regarding context dimensions, different sets of CTT values can be calculated. As a result, all of these trust values can outline the reputation profile of a seller that indicates the dynamic trustworthiness of a seller in different products, product categories, price ranges, time periods, and any necessary combination of them. We name this new model ReputationPro . Nevertheless, in ReputationPro , the computation of reputation profile requires new data structures for appropriately indexing the precomputation of aggregates over large-scale ratings and transaction data in three context dimensions, as well as novel algorithms for promptly answering buyers’ CTT queries. In addition, storing precomputed aggregation results consumes a large volume of space, particularly for a system with millions of sellers. Therefore, reducing storage space for aggregation results is also a great demand. To solve these challenging problems, we first propose a new index scheme CMK-tree by extending the two-dimensional K-D-B-tree that indexes spatial data to support efficient computation of CTT values. Then, we further extend the CMK-tree and propose a CMK-tree RS approach to reducing the storage space allocated to each seller. The two approaches are not only applicable to three context dimensions that are either linear or hierarchical but also take into account the characteristics of the transaction-time model—that is, transaction data is inserted in chronological order. Moreover, the proposed data structures can index each specific product traded in a time period to compute the trustworthiness of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency of computing CTT values to all three existing approaches in the literature. In particular, while answering a buyer’s CTT queries for each brand-based product category, the CMK-tree has almost linear query performance. In addition, with significantly reduced storage space, the CMK-tree RS approach can further improve the efficiency in computing CTT values. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce Web sites in terms of efficiency and storage space consumption.

Community structure detection in large-scale complex networks has been intensively investigated in recent years. In this paper, we propose a new framework which employs the ant colony clustering algorithm based on sampling to discover communities in large-scale complex networks. The algorithm firstly samples a small number of representative nodes from the large-scale network; secondly it uses the ant colony clustering algorithm to cluster the sampled nodes; thirdly it assigns the un-sampled nodes into the detected communities according to the similarity metric; finally it merges the initial clustering result to sustainably increase the modularity function value of the detection results. A significant advantage of our algorithm is that the sampling method greatly reduces the scale of the problem. Experimental results on computer-generated and real-world networks show the efficiency of our method.

Photo recommendation in photo-sharing social networks like Flickr is an important problem. Collaborative filtering is very popular, which assumes each item has the same weight for recommendation. In practice some items are representatives for a class of items and therefore are more important for recommendation. In this paper, we model the importance for items by examining sentiment from the general public towards items. Specifically we propose a model using the temporal dynamic user `favor' information to infer photo importance on Flickr. It is further combined with local community user ratings to improve the Probabilistic Matrix Factorization (PMF) framework for photo recommendation. Experiment results show the effectiveness of the proposed approach.

ABSTRACT In e‐commerce environments, the reputation of sellers is a crucial issue to potential buyers in making decisions. Most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context into account. With such a simple trust evaluation model, a buyer may be easily deceived by a malicious seller in a transaction where the notoriousvalue imbalance problemis involved, namely, the malicious seller can accumulate a high level of trust by selling cheap products and then start to deceive buyers by inducing them to purchase expensive products. In this article, we first present a trust vector consisting of four trust values (termed asCTTvalues), the computation of each of which is based on both past transactions and the forthcoming transaction. In addition, in the computation of CTT values, the parameters, such as product category, price range, and time range, can be specified and adjusted by the buyer, yielding different sets of trust values that can outline the seller's reputation profile. As a result, the value imbalance problem potentially existing in a forthcoming transaction can be identified. The computation of CTT values requires the pre‐computation of aggregates over large‐scale ratings and transaction data with necessary combinations of three dimensions (i.e., product category, price, and time), so as to deliver prompt responses to a buyer's query on different CTT values. In solving this challenging problem, we propose three new data structures and algorithms that mainly focus on efficient computation of CTT values. Our experiments conducted on both eBay data sets and large‐scale synthetic data sets illustrate the advantages and disadvantages of our proposed approaches when responding to a buyer's CTT queries. Copyright © 2014 John Wiley & Sons, Ltd.

Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The “all good reputation” problem, however, is prevalent in current reputation systems-reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.

In e-commerce environments, trust is a dominating factor in seller selection. Most existing trust evaluation studies compute a single value to reflect the "general' or "global" trust level of a seller provider without any contextual transaction information taken into account. As a result, a buyer may be easily deceived by a malicious seller in a forthcoming transaction. For example, with the notorious "value imbalance problem", a malicious seller can build up a high trust level by selling cheap products and then starts to deceive buyers in selling expensive products. To detect this problem and avoid massive monetary losses of buyers, trust evaluation should be associated with both past transactions and the new one, and take transaction context into account. In particular, the computed trust result should outline the seller's reputation profile indicating the trust level in a specific product or a product category, a price range, a time period or any necessary combination of them. However, this need requires complex computation and thus new data structures and efficient algorithms. In this paper, we design a new data structure to support the CTT computation in e-commerce environments. In addition, based on the new data structures, we further propose an approach for promptly responding to a buyer's CTT query. The conducted experiments illustrate that our proposed structure and approach can yield much shorter computation time than the existing approaches.

Maintaining strictness in dimensions is important in integration of data warehouses. A dimension that satisfies all of its roll-up constraints is said to be strict, a property that is required for correct aggregation. Existing work on instance matching does not address the problem of enforcing the strictness of roll-up constraints. In this paper, we use a graph matching-based approach to dimension instance matching and propose an algorithm that enforces strictness and reduces false positives. Making use of similarity flooding, the graph matching algorithm can be greedy in identifying matching members, we propose heuristics to further reduce false positive matches and reduce false strictness. Experiments on real-world data demonstrates the effectiveness of our proposed approach.

The trust of sellers and transactions is a very important issue in e-commerce and e-service environments. At some e-commerce websites (such as eBay1), the trust management mechanism can compute a trust value of a seller, which is based on the ratings of past transactions given by buyers. This trust value, however, is static and can only reflect the general or global trust status of a seller, and it is not directly bound to a new transaction. As a result, a buyer maybe easily cheated by a malicious seller in a new transaction with the notorious value imbalance problem [5], i.e., the malicious seller can build up a good reputation by selling cheap products/services and then start to cheat buyers by selling expensive products/services. Instead of providing such a static trust value, in order to provide more objective trust result for anew potential transaction, a trust evaluation mechanism should based on the ratings of past transactions, the nature of both past transactions and the new transaction. In this paper, we propose a new contextual trust evaluation method. Our method compares the transaction context similarity between the new transaction and past transactions, from which the trust value of the new transaction can be determined. Our method can identify and prevent potentially malicious transactions with the value imbalance problem.

BACKGROUND: Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures. RESULTS: In this paper, algorithms are presented to predict long and short disordered regions in proteins, namely the long disordered region prediction algorithm DRaai-L and the short disordered region prediction algorithm DRaai-S. These algorithms are developed based on the Random Forest machine learning model and the profiles of amino acid indices representing various physiochemical and biochemical properties of the 20 amino acids. CONCLUSION: Experiments on DisProt3.6 and CASP7 demonstrate that some sets of the amino acid indices have strong association with the ordered and disordered status of residues. Our algorithms based on the profiles of these amino acid indices as input features to predict disordered regions in proteins outperform that based on amino acid composition and reduced amino acid composition, and also outperform many existing algorithms. Our studies suggest that the profiles of amino acid indices combined with the Random Forest learning model is an important complementary method for pinpointing disordered regions in proteins.

BACKGROUND: Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies. RESULTS: A new algorithm, IUPforest-L, for predicting long disordered regions using the random forest learning model is proposed in this paper. IUPforest-L is based on the Moreau-Broto auto-correlation function of amino acid indices (AAIs) and other physicochemical features of the primary sequences. In 10-fold cross validation tests, IUPforest-L can achieve an area of 89.5% under the receiver operating characteristic (ROC) curve. Compared with existing disorder predictors, IUPforest-L has high prediction accuracy and is efficient for predicting long disordered regions in large-scale proteomes. CONCLUSION: The random forest model based on the auto-correlation functions of the AAIs within a protein fragment and other physicochemical features could effectively detect long disordered regions in proteins. A new predictor, IUPforest-L, was developed to batch predict long disordered regions in proteins, and the server can be accessed from http://dmg.cs.rmit.edu.au/IUPforest/IUPforest-L.php.

The ability to predict defective modules can help us allocate limited quality assurance resources effectively and efficiently. In this paper, we propose a complexity- based method for predicting defect-prone components. Our method takes three code-level complexity measures as input, namely Lines of Code, McCabe's Cyclomatic Complexity and Halstead's Volume, and classifies components as either defective or non-defective. We perform an extensive study of twelve classification models using the public NASA datasets. Cross-validation results show that our method can achieve good prediction accuracy. This study confirms that static code complexity measures can be useful indicators of component quality.

The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.

In this correspondence, we point out a discrepancy in a recent paper, "data mining static code attributes to learn defect predictors," that was published in this journal. Because of the small percentage of defective modules, using probability of detection (pd) and probability of false alarm (pf) as accuracy measures may lead to impractical prediction models.

Emerging patterns (EPs) were proposed recently to capture changes or dierences betw een datasets: an EP is a multivariate feature whose support increases sharply from a background dataset to a target dataset, and the support ratio is called its gro wth rate.Interesting long EPs often have l o w support; mining suc h EPs from high-dimensional datasets is a great challenge due to the combinatorial explosion of the number of candidates.We propose a Constraint-based EP Miner, ConsEPMiner, that utilizes tw o types of constraints for eectively pruning the search space: External constrain tsare user-giv en minimums on support, growth rate, and growth-rate improvement to con ne the resulting EP set.Inheren t constrain ts | same subset support, top growth rate, and same origin | are deriv ed from the propertiesof EPs and datasets, and are solely for pruning the search space and saving computation.ConsEPMiner can eÆciently mine all EPs at low support on large highdimensional datasets, with low minimums on growth rate and growth-rate improvement.In comparison, the widely known Apriori-like approach is ineective on high-dimensional data.While ConsEPMiner adopts several ideas from Dense-Miner [4], a recent constrain t-based association rule miner, its main new contributions are the introduction of inherent constrain ts and the w ays to use them together with externalconstrain ts for eÆcien t EP mining from dense datasets.Experiments on dense data sho w that, at low support, Con-sEPMiner outperforms the Apriori-like approach b y o r d e r s of magnitude and is more than twice as fast as the Dense-Miner approach.D, supp(X), is jft2DjXtgj jDj .