research-article

Document recommendation in social tagging services

Authors:

Xiaofei HeAuthors Info & Claims

WWW '10: Proceedings of the 19th international conference on World wide web

Pages 391 - 400

https://doi.org/10.1145/1772690.1772731

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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Cited By

Chadalawada ADeepak G(2025)SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of OntologiesKnowledge Graphs and Semantic Web10.1007/978-3-031-81221-7_10(139-152)Online publication date: 13-Feb-2025
https://doi.org/10.1007/978-3-031-81221-7_10
Wang XLi QYu DLi QXu G(2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3643670
Jabeen DKhusro SAnjum N(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
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Index Terms

Document recommendation in social tagging services
1. Information systems
  1. Information retrieval

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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

General Chairs:
Michael Rappa
North Carolina State University, USA
,
Paul Jones
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Juliana Freire
University of Utah, USA
,
Soumen Chakrabarti
Indian Institute of Technology, India

Copyright © 2010 International World Wide Web Conference Committee (IW3C2).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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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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Cited By

Chadalawada ADeepak G(2025)SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of OntologiesKnowledge Graphs and Semantic Web10.1007/978-3-031-81221-7_10(139-152)Online publication date: 13-Feb-2025
https://doi.org/10.1007/978-3-031-81221-7_10
Wang XLi QYu DLi QXu G(2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3643670
Jabeen DKhusro SAnjum N(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
https://doi.org/10.21015/vtcs.v11i1.1305
Liu THu YGao JSun YYin B(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
https://dl.acm.org/doi/10.1145/3631711
Liu THu YWang BSun YGao JYin B(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
https://doi.org/10.1109/TNNLS.2022.3185295
Hagerman CBrath RLangevin S(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
https://doi.org/10.1007/978-3-030-93119-3_13
Hu YChen PLiu TGao JSun YYin B(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
https://doi.org/10.1109/IJCNN52387.2021.9533869
Jahanbakhsh FAwadallah ADumais SXu XO'Brien HFreund LArapakis IHoeber OLopatovska I(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
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Maake BOjo SZuva T(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
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Maake BOjo SZuva T(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
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