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

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WSDM 2021 - Proceedings of the 14th ACM international conference on web search and data mining

WSDM 2021 - Proceedings of the 14th ACM International Confer...

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14th ACM international conference on web search and data mining, WSDM 2021

ISBN: (纸本)9781450382977

the proceedings contain 155 papers. the topics discussed include: some thoughts on computational natural language in the 21st century;population-scale study of human needs during the COVID-19 pandemic: analysis and implications;ad delivery algorithms: the hidden arbiters of political messaging;towards ordinal suicide ideation detection on social media;semi-supervised text classification via self-pretraining;modeling across-context attention for long-tail query classification in e-commerce;on the impact of predicate complexity in crowdsourced classification tasks;beyond point estimate: inferring ensemble prediction variation from neuron activation strength in recommender systems;popularity-opportunity bias in collaborative filtering;leave no user behind: towards improving the utility of recommender systems for non-mainstream users;and deconfounding with networked observational data in a dynamic environment.

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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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Maintaining k-MinHash Signatures over Fully-Dynamic data Streams with Recovery 25

Maintaining k-MinHash Signatures over Fully-Dynamic Data Str...

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

作者： Clementi, Andrea Gualà, Luciano Pepè Sciarria, Luca Straziota, Alessandro University of Rome "Tor Vergata" Department of Enterprise Engineering Rome Italy

ISBN: (纸本)9798400713293

We consider the task of performing Jaccard similarity queries over a large collection of items that are dynamically updated according to a streaming input model. An item here is a subset of a large universe U of elements. A well-studied approach to address this important problem in data mining is to design fast-similarity data sketches. In this paper, we focus on global solutions for this problem, i.e., a single data structure which is able to answer both Similarity Estimation and All-Candidate Pairs queries, while also dynamically managing an arbitrary, online sequence of element insertions and deletions received in input. We introduce and provide an in-depth analysis of a dynamic, buffered version of the well-known k-MinHash sketch. this buffered version better manages critical update operations thus significantly reducing the number of times the sketch needs to be rebuilt from scratch using expensive recovery queries. We prove that the buffered k-MinHash uses O(k log |U |) memory words per subset and that its amortized update time per insertion/deletion is O(k log |U |) with high probability. Moreover, our data structure can return the k-MinHash signature of any subset in O(k) time, and this signature is exactly the same signature that would be computed from scratch (and thus the quality of the signature is the same as the one guaranteed by the static k-MinHash). Analytical and experimental comparisons with the other, state-ofthe-art global solutions for this problem given in [Bury et al.,WSDM'18] show that the buffered k-MinHash turns out to be competitive in a wide and relevant range of the online input parameters. © 2025 Copyright held by the owner/author(s).

关键词： data streams

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MedTransTab: Advancing Medical Cross-Table Tabular data Generation 25

MedTransTab: Advancing Medical Cross-Table Tabular Data Gene...

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

作者： Chen, Yuyan Guo, Qingpei You, Shuangjie Li, Zhixu Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Ant Group Beijing China Georgia Institute of Technology AtlantaGA United States Renmin University of China Suzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798400713293

In medical research, clinical trials are pivotal. While prospective clinical research provides a systematic approach to collecting patient data, it grapples with challenges like long durations, increased costs, and most crucially, data scarcity. To address above-mentioned challenge, this paper introduces a novel approach: using cross-table generation to create relevant data. Unlike existing work focused on single-table operations, our method leverages data from multiple sources across various tables, integrating diverse data types and ensuring data consistency across multiple tables. We develop a new framework, MedTransTab, tailored for cross-table tabular data generation in the medical context. this framework extends our previous efforts and is built upon the newly constructed PMC-Struct, derived from an unstructured PMC-patient dataset. Our MedTransTab can generate high-quality patient records, synthesizing detailed biomedical information to align with real or simulated tables from multiple sources. the experiments show that the proposed method significantly improves performance in cross-table tasks. On the PMC-Struct-Plus dataset, we observe an average improvement of 28.85% in data generation and prediction. Similarly, on the Out-Of-Domain (OOD) dataset, there's an average improvement of 22.56%, indicating substantial progress in medical data analysis. © 2025 Copyright held by the owner/author(s).

关键词： data consistency

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Adjacent Neighborhood Transformer-based Diffusion Model for Anomaly Detection under Incomplete Industrial data Sources 25

Adjacent Neighborhood Transformer-based Diffusion Model for ...

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

作者： Wang, Lulu Li, Chengqing School of Mathematics and Computational Science Xiangtan University Xiangtan China School of Computer Science Xiangtan University Xiangtan China

ISBN: (纸本)9798400713293

Anomaly detection in an industrial setting is crucial for operational monitoring, yet it remains challenging under incomplete data conditions. Common issues, such as sensor failures, data transmission loss, and storage malfunctions, often result in missing data, complicating the detection of anomalies, particularly when these are localized within specific regions. To address the challenges, this paper proposes DiffANT, an unsupervised anomaly detection method that integrates a diffusion model with the Adjacent Neighborhood Transformer (ANT). Specifically, DiffANT begins by applying various data masking techniques to simulate realistic missing values that reflect real-world industrial scenarios. the ANT utilizes a Transformer encoder architecture augmented with an adjacent neighborhood attention mechanism. It effectively focuses on relevant non-immediate vicinities to enhance anomaly detection. DiffANT then reconstructs the original data from randomly sampled noise through diffusion and denoising processes and utilizes a multi-level reconstruction strategy to refine the generated samples. We demonstrate the efficacy of DiffANT through extensive experiments in diverse industrial applications, such as secure water treatment and server machine monitoring. the results indicate that DiffANT consistently outperforms state-of-the-art methods in detecting anomalies in time series data, regardless of whether the data is incomplete. © 2025 Copyright held by the owner/author(s).

关键词： Time series

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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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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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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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WSDM 2017 - Proceedings of the 10th ACM international conference on web search and data mining

WSDM 2017 - Proceedings of the 10th ACM International Confer...

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10th ACM international conference on web search and data mining, WSDM 2017

ISBN: (纸本)9781450346757

the proceedings contain 93 papers. the topics discussed include: how smart does your profile image look? estimating intelligence from social network profile images;anticipating information needs based on check-in activity;RedQueen: an online algorithm for smart broadcasting in social networks;uncovering the dynamics of crowdlearning and the value of knowledge;the influence of early respondents: information cascade effects in online event scheduling;evolution of ego-networks in social media with link recommendations;learning parametric models for context-aware query auto-completion via Hawkes processes;does document relevance affect the searcher's perception of time?;investigation of user search behavior while facing heterogeneous search services;a concise integer linear programming formulation for implicit search result diversification;a comparison of document-at-a-time and score-at-a-time query evaluation;ANNE: improving source code search using entity retrieval approach;partitioning and segment organization strategies for real-time selective search on document streams;reliable medical diagnosis from crowdsourcing: discover trustworthy answers from non-experts;PRED: periodic region detection for mobility modeling of social media users;beyond the words: predicting user personality from heterogeneous information;German typographers vs. German grammar: decomposition of Wikipedia category labels into attribute-value pairs;constructing and embedding abstract event causality networks from text snippets;groove radio: a Bayesian hierarchical model for personalized playlist generation;and online matrix completion for signed link prediction.

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