research-article

Tag-aware recommender systems by fusion of collaborative filtering algorithms

Authors:

Karen H. L. Tso-Sutter,

Leandro Balby Marinho,

Lars Schmidt-ThiemeAuthors Info & Claims

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Pages 1995 - 1999

https://doi.org/10.1145/1363686.1364171

Published: 16 March 2008 Publication History

Abstract

Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.

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Lin FZhao ZZhu XZhang DShen SLi XXu TZhang SChen EBaeza-Yates RBonchi F(2024)When Box Meets Graph Neural Network in Tag-aware RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671973(1770-1780)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671973
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Zuo YLiu SZhou YLiu H(2023)TRAL: A Tag-Aware Recommendation Algorithm Based on Attention LearningApplied Sciences10.3390/app1302081413:2(814)Online publication date: 6-Jan-2023
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Tag-aware recommender systems by fusion of collaborative filtering algorithms
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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cover image ACM Conferences

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

March 2008

2586 pages

ISBN:9781595937537

DOI:10.1145/1363686

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Hisham M. Haddad
Kennesaw State University

Copyright © 2008 ACM.

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Published: 16 March 2008

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SAC '08: The 2008 ACM Symposium on Applied Computing

March 16 - 20, 2008

Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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https://dl.acm.org/doi/10.1145/3637528.3671973
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https://doi.org/10.3390/app13052945
Zuo YLiu SZhou YLiu H(2023)TRAL: A Tag-Aware Recommendation Algorithm Based on Attention LearningApplied Sciences10.3390/app1302081413:2(814)Online publication date: 6-Jan-2023
https://doi.org/10.3390/app13020814
Papadakis HPapagrigoriou AKosmas EPanagiotakis CMarkaki SFragopoulou P(2023)Content-Based Recommender Systems TaxonomyFoundations of Computing and Decision Sciences10.2478/fcds-2023-000948:2(211-241)Online publication date: 30-Jun-2023
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