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Document recommendation in social tagging services

Published: 26 April 2010 Publication History

Abstract

Social tagging services allow users to annotate various online resources with freely chosen keywords (tags). They not only facilitate the users in finding and organizing online resources, but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems. Traditional studies on recommender systems focused on user rating data, while recently social tagging data is becoming more and more prevalent. How to perform resource recommendation based on tagging data is an emerging research topic. In this paper we consider the problem of document (e.g. Web pages, research papers) recommendation using purely tagging data. That is, we only have data containing users, tags, documents and the relationships among them. We propose a novel graph-based representation learning algorithm for this purpose. The users, tags and documents are represented in the same semantic space in which two related objects are close to each other. For a given user, we recommend those documents that are sufficiently close to him/her. Experimental results on two data sets crawled from Del.icio.us and CiteULike show that our algorithm can generate promising recommendations and outperforms traditional recommendation algorithms.

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  • (2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 29-Jan-2024
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    Published In

    cover image ACM Other conferences
    WWW '10: Proceedings of the 19th international conference on World wide web
    April 2010
    1407 pages
    ISBN:9781605587998
    DOI:10.1145/1772690

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 April 2010

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    Author Tags

    1. recommender systems
    2. social tagging

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    WWW '10
    WWW '10: The 19th International World Wide Web Conference
    April 26 - 30, 2010
    North Carolina, Raleigh, USA

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    • (2023)Research in Collaborative Tagging Applications: Choosing the Right DatasetVAWKUM Transactions on Computer Sciences10.21015/vtcs.v11i1.130511:1(01-25)Online publication date: 5-Mar-2023
    • (2023)Cross-modal Multiple Granularity Interactive Fusion Network for Long Document ClassificationACM Transactions on Knowledge Discovery from Data10.1145/363171118:4(1-24)Online publication date: 6-Nov-2023
    • (2023)Hierarchical Graph Convolutional Networks for Structured Long Document ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318529534:10(8071-8085)Online publication date: Oct-2023
    • (2022)VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning ClassifierIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_13(337-357)Online publication date: 5-Jun-2022
    • (2021)Hierarchical Attention Transformer Networks for Long Document Classification2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533869(1-7)Online publication date: 2021
    • (2020)Effects of Past Interactions on User Experience with Recommended DocumentsProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3377977(153-162)Online publication date: 14-Mar-2020
    • (2019)A Survey on Data Mining Techniques in Research Paper Recommender SystemsResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch006(119-143)Online publication date: 2019
    • (2019)Information Processing in Research Paper Recommender System ClassesResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch005(90-118)Online publication date: 2019
    • (2019)A Deep Learning Framework to Predict Rating for Cold Start Item Using Item Metadata2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE.2019.00071(313-319)Online publication date: Jun-2019
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