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检索条件"任意字段=16th International Conference on Web Search and Data Mining"

共 3942 条记录，以下是41-50 订阅

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Analyzing X's web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures 16th

Analyzing X's Web of Influence: Dissecting News Sharing Dyna...

引用

16th international conference on Social Networks Analysis and mining

作者： Abdidizaji, Sina Baekey, Alexander Jayalath, Chathura Mantzaris, Alexander Garibay, Ozlem Ozmen Garibay, Ivan Univ Cent Florida Ind Engn & Management Syst Orlando FL 32816 USA Univ Cent Florida Comp Sci Orlando FL 32816 USA Univ Cent Florida Stat & Data Sci Orlando FL 32816 USA

ISBN: (纸本)9783031785405;9783031785412

the dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. the challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). this study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. the methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. this asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence.

关键词： Influential Actors News Articles Social Network Analysis Transfer Entropy Multiplex Networks

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Towards Secure and Robust Recommender Systems: A data-Centric Perspective 25

Towards Secure and Robust Recommender Systems: A Data-Centri...

引用

18th ACM international conference on web search and data mining, WSDM 2025

作者： Wang, Zongwei Yu, Junliang Chen, Tong Yin, Hongzhi Sadiq, Shazia Gao, Min Chongqing University Chongqing China The University of Queensland Brisbane Australia

ISBN: (纸本)9798400713293

As recommender systems (RS) continue to evolve, the field has seen a pivotal shift from model-centric to data-centric paradigms, where the quality, integrity, and security of data are increasingly becoming the key drivers of system performance and personalization. this transformation has unlocked new avenues for more precise recommendations, yet it also introduces significant challenges. As reliance on data intensifies, RS face mounting threats that can compromise both their effectiveness and user trust. these challenges include (1) Malicious data Manipulation, where adversaries corrupt or tamper with datasets, distorting recommendation outcomes and undermining system reliability;(2) data Privacy Leakage, where adversarial actors exploit system outputs to infer sensitive user information, leading to serious privacy concerns;and (3) Erroneous data Noise, where inaccuracies, inconsistencies, and redundant data obscure the true user preferences, degrading recommendation quality and user satisfaction. By focusing on these critical data-centric challenges, this tutorial aims to equip participants with the knowledge to build RS that are secure, privacy-preserving, and resilient to data-driven threats, ensuring reliable and trustworthy performance in real-world environments. In addition, attendees will gain hands-on experience with our newly released toolkit for RS-based attacks and defenses, providing them with practical, actionable insights into safeguarding RS against emerging vulnerabilities. © 2025 Copyright held by the owner/author(s).

关键词： Information leakage

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Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding 25

Dynamic Graph Transformer with Correlated Spatial-Temporal P...

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18th ACM international conference on web search and data mining, WSDM 2025

作者： Wang, Zhe Zhou, Sheng Chen, Jiawei Zhang, Zhen Hu, Binbin Feng, Yan Chen, Chun Wang, Can Zhejiang University College of Computer Science Hangzhou China Zhejiang University School of Software Technology Ningbo China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China National University of Singapore Singapore Singapore Ant Group Hangzhou China

ISBN: (纸本)9798400713293

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT. the code is available at: https://***/wangz3066/CorDGT. © 2025 Copyright held by the owner/author(s).

关键词： Encoding (symbols)

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Robust Information Retrieval 25

Robust Information Retrieval

引用

18th ACM international conference on web search and data mining, WSDM 2025

作者： Liu, Yu-An Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400713293

Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only deliver strong performance on average but also have the ability to handle a variety of exceptional situations. In recent years, research into the robustness of IR has seen significant growth, with numerous researchers offering extensive analyses and proposing myriad strategies to address robustness challenges. In this tutorial, we first provide background information covering the basics and a taxonomy of robustness in IR. then, we examine adversarial robustness and out-of-distribution (OOD) robustness within IR-specific contexts, extensively reviewing recent progress in methods to enhance robustness. the tutorial concludes with a discussion on the robustness of IR in the context of large language models (LLMs), highlighting ongoing challenges and promising directions for future research. this tutorial aims to generate broader attention to robustness issues in IR, facilitate an understanding of the relevant literature, and lower the barrier to entry for interested researchers and practitioners. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation 25

Oracle-guided Dynamic User Preference Modeling for Sequentia...

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18th ACM international conference on web search and data mining, WSDM 2025

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Amazon Seattle United States School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China

ISBN: (纸本)9798400713293

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the "oracle" user preferences in the future and will be beneficial to model dynamic user preferences. therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn "forward-looking" models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin. © 2025 Copyright held by the owner/author(s).

关键词： dynamic user preference modeling

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Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous data with RAG Systems 25

Evidence Contextualization and Counterfactual Attribution fo...

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18th ACM international conference on web search and data mining, WSDM 2025

作者： Saha Roy, Rishiraj Schlotthauer, Joel Hinze, Chris Foltyn, Andreas Hahn, Luzian Kuech, Fabian AME Fraunhofer IIS Bavaria Erlangen Germany

ISBN: (纸本)9798400713293

Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA). In a RAG system, a retriever fetches passages from a collection in response to a question, which are then included in the prompt of a large language model (LLM) for generating a natural language (NL) answer. However, several RAG systems today suffer from two shortcomings: (i) retrieved passages usually contain their raw text and lack appropriate document context, negatively impacting both retrieval and answering quality;and (ii) attribution strategies that explain answer generation typically rely only on similarity between the answer and the retrieved passages, thereby only generating plausible but not causal explanations. In this work, we demonstrate RAGonite, a RAG system that remedies the above concerns by: (i) contextualizing evidence with source metadata and surrounding text;and (ii) computing counterfactual attribution, a causal explanation approach where the contribution of an evidence to an answer is determined by the similarity of the original response to the answer obtained by removing that evidence. To evaluate our proposals, we release a new benchmark ConfQuestions: it has 300 hand-created conversational questions, each in English and German, coupled with ground truth URLs, completed questions, and answers from 215 public Confluence pages. these documents are typical of enterprise wiki spaces with heterogeneous elements. Experiments with RAGonite on ConfQuestions show the viability of our ideas: contextualization improves RAG performance, and counterfactual explanations outperform standard attribution. © 2025 Copyright held by the owner/author(s).

关键词： Question answering

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DTPN: A Diffusion-based Traffic Purification Network for Tor website Fingerprinting 25

DTPN: A Diffusion-based Traffic Purification Network for Tor...

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18th ACM international conference on web search and data mining, WSDM 2025

作者： Yang, Chenchen Xiao, Xi Hu, Guangwu Ling, Zhen Li, Hao Zhang, Bin Shenzhen International Graduate School Tsinghua University Shenzhen China Key Laboratory of Cyberspace Security Ministry of Education of China Zhengzhou China Peng Cheng Laboratory Shenzhen China Shenzhen Institute of Information Technology Shenzhen China Southeast University Nanjing China National Key Laboratory of Advanced Communication Network Shijiazhuang China

ISBN: (纸本)9798400713293

website Fingerprinting attack is a type of method used to classify network traffic generated by users on the Tor (the Onion Router) based on the websites they visit, leading to the leakage of individuals' privacy . For website Fingerprinting attack, network traffic defense methods involve adding noise to the original network traffic to render the attacker's methods ineffective. Previous attack methods primarily focused on improving classification accuracy by enhancing the attack model, with adversarial training being the most common approach. However, adversarial training requires frequent updates and exhibits poor generalization when dealing with previously unseen network traffic protection methods. In order to address the limitations of adversarial training, a novel method is proposed leveraging a diffusion model for network traffic purification. this paper is the first to use a diffusion model to resist network traffic defense based on adversarial perturbations. the diffusion models are theoretically suited for data purification in the training mode, i.e., removing noises generated by adversarial perturbations from the data. Our method enables existing network traffic classification methods to maintain effective classification of network traffic after protection without requiring retraining, while also achieving good generalization performance with previously unseen network traffic defense methods. the purified network traffic data can effectively improve the robustness of existing website fingerprinting methods. Experiments conducted under various network traffic defense strategies demonstrate that the proposed method increases accuracy by up to 60.8% on DF dataset and 50.3% on CW100 dataset, respectively, compared to adversarial training. © 2025 Copyright held by the owner/author(s).

关键词： websites

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LLM4Eval@WSDM 2025: Large Language Model for Evaluation in Information Retrieval 25

LLM4Eval@WSDM 2025: Large Language Model for Evaluation in I...

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18th ACM international conference on web search and data mining, WSDM 2025

作者： Rahmani, Hossein A. Siro, Clemencia Aliannejadi, Mohammad Craswell, Nick Clarke, Charles L.A. Faggioli, Guglielmo Mitra, Bhaskar thomas, Paul Yilmaz, Emine University College London London United Kingdom University of Amsterdam Amsterdam Netherlands Microsoft Bellevue United States University of Waterloo WaterlooON Canada University of Padua Padua Italy Microsoft Montréal Canada Microsoft Adelaide Australia Amazon London United Kingdom

ISBN: (纸本)9798400713293

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. the main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. the format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference. this is the second iteration of the workshop. the first version was held in conjunction with SIGIR 2024, attracting over 50 participants. © 2025 Copyright held by the owner/author(s).

关键词： search engines

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WSDM 2015 - Proceedings of the 8th ACM international conference on web search and data mining

WSDM 2015 - Proceedings of the 8th ACM International Confere...

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7th ACM SIGMM international Workshop on Multimedia Information Retrieval, MIR 2005

ISBN: (纸本)1595932445

the proceedings contain 32 papers. the topics discussed include: multimedia information retrieval: what it is, and why isn't anyone using it?;active semi-supervised fuzzy clustering for image database categorization;low-cost distributed learning of a Gaussian mixture model for multimedia content-based indexing on a peer-to-peer network;similarity space projection for web image search and annotation;probabilistic web image gathering;multi-graph enabled active learning for multimodal web image retrieval;inferring similarity between music objects with application to playlist generation;framework for personalized multimedia summarization;enhanced ontologies for video annotation and retrieval;and semi-automatic video annotation based on active learning with multiple complementary predictors.

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L2D 2021 - Proceedings of the 1st international Workshop on Enabling data-Driven Decisions from Learning on the web, co-located with the 14th ACM international conference on web search and data mining, WSDM 2021

L2D 2021 - Proceedings of the 1st International Workshop on ...

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1st international Workshop on Enabling data-Driven Decisions from Learning on the web, L2D 2021

the proceedings contain 6 papers. the topics discussed include: incorporating wide context information for deep knowledge tracing using attentional bi-interaction;collaboration patterns in students’ teams working on business cases;modeling student behavior in synchronous online learning during the COVID-19 pandemic;learning testing model using test generators and mobile applications;test students’ attention in class using a mobile application;and designing intelligent systems for online education: open challenges and future directions.

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