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

共 3936 条记录，以下是1-10 订阅

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WSDM 2023 - Proceedings of the 16th ACM international conference on web search and data mining

WSDM 2023 - Proceedings of the 16th ACM International Confer...

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16th ACM international conference on web search and data mining, WSDM 2023

ISBN: (纸本)9781450394079

the proceedings contain 183 papers. the topics discussed include: local edge dynamics and opinion polarization;never too late to learn: regularizing gender bias in coreference resolution;marginal-certainty-aware fair ranking algorithm;beyond digital ‘echo chambers’: the role of viewpoint diversity in political discussion;mining user-aware multi-relations for fake news detection in large scale online social networks;mining user-aware multi-relations for fake news detection in large scale online social networks;self-supervised group graph collaborative filtering for group recommendation;towards universal cross-domain recommendation;a personalized neighborhood-based model for within-basket recommendation in grocery shopping;disentangled negative sampling for collaborative filtering;relation preference oriented high-order sampling for recommendation;and minimum entropy principle guided graph neural networks.

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WSDM 2025 - Proceedings of the 18th ACM international conference on web search and data mining

WSDM 2025 - Proceedings of the 18th ACM International Confer...

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

ISBN: (纸本)9798400713293

the proceedings contain 150 papers. the topics discussed include: graph disentangle causal model: enhancing causal inference in networked observational data;CIMAGE: exploiting the conditional independence in masked graph auto-encoders;a sublinear algorithm for approximate shortest paths in large networks;POLARIS: sampling from the multigraph configuration model with prescribed color assortativity;DiffGraph: heterogeneous graph diffusion model;mitigating overfitting in graph neural networks via feature and hyperplane perturbation;dynamic graph transformer with correlated spatial-temporal positional encoding;S-Diff: an anisotropic diffusion model for collaborative filtering in spectral domain;hyperdimensional representation learning for node classification and link prediction;prospective multi-graph cohesion for multivariate time series anomaly detection;and a contrastive framework with user, item and review alignment for recommendation.

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IntelliSMART: Intelligent Semantic Machine-Assisted Research Tool 16th

IntelliSMART: Intelligent Semantic Machine-Assisted Research...

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16th international conference on Social Networks Analysis and mining

作者： Khatri, Aadyant Egierski, Nicolas Pochamreddy, Ashutosh Alhamadani, Abdulaziz Sarkar, Shailik Lu, Chang-Tien Dept Comp Sci Virginia Tech Falls Church VA 22043 USA Florida Polytech Univ Dept Data Sci & Business Analyt Lakeland FL USA

ISBN: (纸本)9783031785535;9783031785542

the exponential growth of academic literature presents significant challenges for researchers attempting to find relevant information. Traditional keyword-based retrieval systems often fail to address issues such as synonyms, homonyms, and semantic nuances, leading to suboptimal search results. this paper introduces a novel system called IntelliSMART (Intelligent Semantic Machine-Assisted Research Tool), which leverages large language models (LLMs) and advanced semantic processing techniques to improve the retrieval of academic literature. Our approach integrates query rewriting, embedding generation, efficient indexing, and complex article retrieval mechanisms to provide highly accurate and contextually relevant results that align with the user's intent. the IntelliSMART system features a user-friendly front end that facilitates intuitive query input, along with a robust back end for handling user queries, generating embeddings, indexing extensive collections of academic papers, and efficiently retrieving the most relevant documents. the proposed system shows significant improvements over conventional methods, highlighting its potential to transform the search experience in academic research.

关键词： data mining Information retrieval Semantic search Large language models Keyword search Text Generation

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IWILDS'25: the 5th international Workshop on Investigating Learning During web search 25

IWILDS'25: The 5th International Workshop on Investigating L...

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

作者： Hoppe, Anett Yu, Ran Liu, Jiqun Bhattacharya, Nilavra TIB - Leibniz Information Centre for Science and Technology Hannover Germany L3S Research Center Leibniz Universität Hannover Hannover Germany University of Bonn Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence *** Bonn Germany The University of Oklahoma NormanOK United States ABB AG Corporate Research Center Germany Mannheim Germany

ISBN: (纸本)9798400713293

web-based learning is evolving rapidly as traditional search engines are complemented by Large Language Models (LLMs) and other AI technologies. this evolution offers new opportunities, such as automated information synthesis and personalized learning experiences. However, this also presents new challenges, including the need for learners to be aware of potential biases and misinformation in AI-generated content, and to maintain focus and depth in their learning journeys. the search as Learning (SAL) field investigates how individuals learn through web interactions, examining the interplay between search behaviors, information synthesis, and learning outcomes. SAL research aims to measure, predict, and support effective learning strategies in modern web environments. IWILDS'25 provides an interdisciplinary platform to explore these dynamics, bringing together researchers and practitioners from information retrieval, education, psychology, and related fields. this full-day workshop will feature keynotes, paper presentations, and discussions on advancing SAL research and practice. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Cluster Insight: A Weighted Clustering Tool for Large Textual data Exploration 25

Cluster Insight: A Weighted Clustering Tool for Large Textua...

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

作者： Ferdjaoui, Amine Affeldt, Séverine Nadif, Mohamed SogetiLabs Paris France Centre Borelli Université Paris Cité Paris France Centre Borelli UMR 9010 Université Paris Cité Paris France

ISBN: (纸本)9798400713293

In unsupervised learning, the exploration of large volumes of textual data is a topic of significant interest. In this article, we present our compact and easy-to-use application to explore large volumes of textual data using clustering and generative models. We demonstrate how to adapt the Lasso weighted k-means algorithm to handle textual data. In addition, we present in detail a user-friendly package that shows how to use LLMs effectively to describe document classes. © 2025 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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Don't Forget this: Augmenting Results with Event-Aware search 25

Don't Forget This: Augmenting Results with Event-Aware Searc...

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

作者： Sousa, Hugo Ward, Austin R. Alonso, Omar University of Porto INESC TEC Porto Portugal Amazon SeattleWA United States Amazon Palo AltoCA United States

ISBN: (纸本)9798400713293

Events like Valentine's Day and Christmas can influence user intent when interacting with search engines. For example, a user searching for gift around Valentine's Day is likely to be looking for Valentine's-themed options, whereas the same query close to Christmas would more likely suggest an interest in Holiday-themed gifts. these shifts in user intent, driven by temporal factors, are often implicit but important to determine the relevance of search results. In this demo, we explore how incorporating temporal awareness can enhance search relevance in an e-commerce setting. We constructed a database of 2K events and, using historical purchase data, developed a temporal model that estimates each event's importance on a specific date. the most relevant events on the date the query was issued are then used to enrich search results with event-specific items. Our demo illustrates how this approach enables a search system to better adapt to temporal nuances, ultimately delivering more contextually relevant products. © 2025 Copyright held by the owner/author(s).

关键词： search engines

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16th international conference on Social Networks Analysis and mining, ASONAM 2024

16th International Conference on Social Networks Analysis an...

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16th international conference on Social Networks Analysis and mining, ASONAM 2024

ISBN: (纸本)9783031785375

the proceedings contain 109 papers. the special focus in this conference is on Social Networks Analysis and mining. the topics include: Beyond Boundaries: Capturing Social Segregation on Hypernetworks;burstiness in Emotions: A Case Study on Collective Affective Responses in Italian Soccer Fandoms;exploring Crisis-Driven Social Media Patterns: A Twitter dataset of Usage During the Russo-Ukrainian War;PARALLAX: Leveraging Polarization Knowledge for Misinformation Detection;dynamic Inter-organizational Communication Network in a Post-merger Integration;analyzing X’s web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures;fairNet: A Genetic Framework to Reduce Marginalization in Social Networks;Agent-Based Modelling Meets Generative AI in Social Network Simulations;deepFairRank: A Multi-objective Framework for Fair Top-k Node Ranking in Network data;RUMs with Ties: A Discrete Choice Model Allowing Multiple Winners;thresholds as Mechanisms for Weighting Influence in the Linear threshold Rank;parfaite: PageRank-Matrix Factorization for Interpretable Graph Embeddings;Categorising Corruption in the Vaccine Discourse: A General Taxonomy, data Set, and Evaluation of LLMs for Classifying Corruption Dialogue in Social Media;evaluating and Improving Projects’ Bus-Factor: A Network Analytical Framework;towards Generalized Offensive Language Identification;online Social Community Neighborhood Formation;enhancing Stance Classification on Social Media Using Quantified Moral Foundations;on mining Dynamic Graphs for k Shortest Paths;WIBA: What Is Being Argued? A Comprehensive Approach to Argument mining;FReCS: A First Responder Classification System;exploring Behavioral Tendencies on Social Media: A Perspective through Claim Check-Worthiness;you Must Be a Trump Supporter: Political Identity Projections on the Social web;node Generation for Node Classification in Sparsely-Labeled Graphs;the Dawn of Decen

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16th international conference on Social Networks Analysis and mining, ASONAM 2024