research-article

A Random Walk Model for Item Recommendation in Social Tagging Systems

Authors:

Daniel D. Zeng,

Xiaolong ZhengAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 4, Issue 2

Article No.: 8, Pages 1 - 24

https://doi.org/10.1145/2490860

Published: 01 August 2013 Publication History

Abstract

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.

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Index Terms

A Random Walk Model for Item Recommendation in Social Tagging Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 4, Issue 2

August 2013

113 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2499962

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Copyright © 2013 ACM.

© 2013 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 01 August 2013

Accepted: 01 May 2013

Revised: 01 March 2013

Received: 01 April 2012

Published in TMIS Volume 4, Issue 2

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Wu ZDai XWang XXiong YGao SLiu D(2023)A Multi-Label Recommendation Algorithm Based on Graph Attention and Sentiment Correction2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)10.1109/AINIT59027.2023.10212701(396-401)Online publication date: 16-Jun-2023
https://doi.org/10.1109/AINIT59027.2023.10212701
Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Puri GVaranasi ASingh GAgarwal HTomar RChoudhury T(2023)Investigating Graph-Based Recommendations Systems and Graph Traversal AlgorithmsMicro-Electronics and Telecommunication Engineering10.1007/978-981-19-9512-5_11(115-126)Online publication date: 2-Jun-2023
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Pande CWitschel HMartin A(2022)New Hybrid Techniques for Business Recommender SystemsApplied Sciences10.3390/app1210480412:10(4804)Online publication date: 10-May-2022
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Shen MWang JLiu OWang H(2020)Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging SystemsJournal of Database Management10.4018/JDM.202010010231:4(24-45)Online publication date: 1-Oct-2020
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