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检索条件"任意字段=11th ACM International Conference on Web Search and Data Mining"

共 1878 条记录，以下是31-40 订阅

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Enhancing Code search Intent with Programming Context Exploration 25

Enhancing Code Search Intent with Programming Context Explor...

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

作者： Dong, Yanmin Huang, Zhenya Zhang, Zheng Zhao, Guanhao Wu, Likang Zhao, Hongke Jin, Binbin Liu, Qi State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China College of Management and Economics Tianjin University Tianjin China

ISBN: (纸本)9798400713293

An intelligent code search engine tries to find and suggest a code piece given a developer's query quickly from a large-scale program database, which can significantly promote software development efficiency. Existing solutions can search the relevant codes to some extent. However, most of them fail to precisely understand the search intent of developers since they only mine their natural language queries, while ignoring the valuable programming context (e.g., the code written by the developer). In this paper, we study the problem of context-aware code search. To promote a step forward, we first provide the CodesearchNet-C dataset with constructing sufficient programming context from the Github web repository for each query-code instance. the dataset is supplemented on the CodesearchNet benchmark, ensuring both generality and comparability for relevant research. then, by analyzing the characteristics of programming context, we propose a novel two-stage Context-aware Code Retrieval (ConCR) framework. In the first stage, we propose a Context Walking algorithm, which simulates the programming habits of different developers. the generated programming context could ensure the diversity of search intent among developers. In the second stage, imitating the reading habits of developers, we introduce a novel Context Hierarchical Encoder, to understand the search intent with contextual information from local to global. Our ConCR framework is general, and we give three implementations on the basis of typical code search models as backbones. Extensive experimental results clearly prove that our ConCR significantly enhances the code search performance, effectively fulfilling developers' needs for efficient code resource searching on the web. these results also verify the necessity of introducing programming context to understand developers' intent. © 2025 Copyright held by the owner/author(s).

关键词： Structured Query Language

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How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective 25

How Do Recommendation Models Amplify Popularity Bias? An Ana...

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

作者： Lin, Siyi Gao, Chongming Chen, Jiawei Zhou, Sheng Hu, Binbin Feng, Yan Chen, Chun Wang, Can Zhejiang University Hangzhou China University of Science and Technology of China Hefei China Ant Group Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9798400713293

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation of popular items in the recommendation lists. this study conducts comprehensive empirical and theoretical analyses to expose the root causes of this phenomenon, yielding two core insights: 1) Item popularity is memorized in the principal spectrum of the score matrix predicted by the recommendation model;2) the dimension reduction phenomenon amplifies the relative prominence of the principal spectrum, thereby intensifying the popularity bias. Building on these insights, we propose a novel debiasing strategy that leverages a spectral norm regularizer to penalize the magnitude of the principal singular value. We have developed an efficient algorithm to expedite the calculation of the spectral norm by exploiting the spectral property of the score matrix. Extensive experiments across seven real-world datasets and three testing paradigms have been conducted to validate the superiority of the proposed method. © 2025 Copyright held by the owner/author(s).

关键词： popularity bias

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Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational data 25

Graph Disentangle Causal Model: Enhancing Causal Inference i...

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

作者： Hu, Binbin An, Zhicheng Wu, Zhengwei Tu, Ke Liu, Ziqi Zhang, Zhiqiang Zhou, Jun Feng, Yufei Chen, Jiawei Ant Group Hangzhou China Alibaba Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798400713293

Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influence of hidden confounders, which remain unobserved at the individual unit level. To address this limitation, researchers have utilized graph neural networks to aggregate neighbors' features to capture the hidden confounders and mitigate confounding bias by minimizing the discrepancy of confounder representations between the treated and control groups. Despite the success of these approaches, practical scenarios often treat all features as confounders and involve substantial differences in feature distributions between the treated and control groups. Confusing the adjustment and confounder and enforcing strict balance on the confounder representations could potentially undermine the effectiveness of outcome prediction. To mitigate this issue, we propose a novel framework called the Graph Disentangle Causal model (GDC) to conduct ITE estimation in the network setting. GDC utilizes a causal disentangle module to separate unit features into adjustment and confounder representations. then we design a graph aggregation module consisting of three distinct graph aggregators to obtain adjustment, confounder, and counterfactual confounder representations. Finally, a causal constraint module is employed to enforce the disentangled representations as true causal factors. the effectiveness of our proposed method is demonstrated by conducting comprehensive experiments on two networked datasets. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research data Management 25

Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enab...

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

作者： Sakor, Ahmad Brunet, Mauricio Iglesias, Enrique Rivas, Ariam Rohde, Philipp D. Kraft, Angelina Vidal, Maria-Esther TIB - Leibniz Information Centre for Science and Technology Hannover Germany Leibniz University Hannover Hannover Germany L3S Research Center Hannover Germany

ISBN: (纸本)9798400713293

Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. the Leibniz data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. this demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://***/ldmservice/. © 2025 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Predicting Eviction Status Using Airbnb data in the Absence of Ground-Truth Eviction Records 25

Predicting Eviction Status Using Airbnb Data in the Absence ...

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

作者： Tabar, Maryam Abdulla, Anusha Petersen, J. Andrew Lee, Dongwon University of Texas at San Antonio San AntonioTX United States The Pennsylvania State University University ParkPA United States

ISBN: (纸本)9798400713293

the eviction of tenants is a pressing problem, which is prevalent among low-income renters in the USA, and has devastating consequences. Despite the presence of various measures to combat evictions, identifying high-need regions and tenant groups is highly challenging in many regions due to a lack of access to eviction records (partly because of some infrastructural/policy constraints). In response to this information gap, this paper proposes a solution driven by Machine Learning (ML) to monitor eviction status at various spatial resolutions using Airbnb data when ground-truth eviction data is inaccessible. In particular, we begin by demonstrating the potential of utilizing Airbnb data to build ML-driven methods for distinguishing different neighborhoods across different spatial resolutions with respect to eviction status. We then proceed to develop an ML model capable of learning eviction status levels from Airbnb data, even in the absence of ground-truth labels. Empirical evidence is presented, showcasing the model's performance on par with several robust fully-supervised ML models that had access to ground-truth labels during training. Finally, we conduct a set of cross-region tests to comprehensively study the generalizability of the achieved performance across various unseen regions in the USA that were not used during model training. the code of this project can be accessed via https://***/maryam-tabar/Airbnb-Eviction. © 2025 Copyright held by the owner/author(s).

关键词： learning under lack of ground-truth labels

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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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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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Robust Information Retrieval 25

Robust Information Retrieval

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

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

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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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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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