research-article

An exploration of improving collaborative recommender systems via user-item subgroups

Authors:

Deng CaiAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 21 - 30

https://doi.org/10.1145/2187836.2187840

Published: 16 April 2012 Publication History

Abstract

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.

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Index Terms

An exploration of improving collaborative recommender systems via user-item subgroups
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. World Wide Web
    1. Web applications
    2. Web services

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cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

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Published: 16 April 2012

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WWW 2012

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WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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https://doi.org/10.1007/s11263-024-02157-w
Gharibi SBagheriFard KParvin HNejatian SYaghoubyan S(2024)Ontology-based recommender system: a deep learning approachThe Journal of Supercomputing10.1007/s11227-023-05874-0Online publication date: 7-Feb-2024
https://doi.org/10.1007/s11227-023-05874-0
Chen YZhou XChen LWen JYang CZhang SLiang W(2024)Practical Teaching Reform of Cybersecurity Courses Incorporating Personalized RecommendationsComputer Science and Education. Teaching and Curriculum10.1007/978-981-97-0791-1_4(41-54)Online publication date: 26-Feb-2024
https://doi.org/10.1007/978-981-97-0791-1_4
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