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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G. Silva MC. Madeira SHenriques R(2024)A Comprehensive Survey on Biclustering-based Collaborative FilteringACM Computing Surveys10.1145/367472356:12(1-32)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3674723
Wang ZXu QYang ZWen PHe YCao XHuang Q(2024)Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-label ClassificationInternational Journal of Computer Vision10.1007/s11263-024-02157-wOnline publication date: 26-Jul-2024
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
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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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Published In

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Cited By

G. Silva MC. Madeira SHenriques R(2024)A Comprehensive Survey on Biclustering-based Collaborative FilteringACM Computing Surveys10.1145/367472356:12(1-32)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3674723
Wang ZXu QYang ZWen PHe YCao XHuang Q(2024)Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-label ClassificationInternational Journal of Computer Vision10.1007/s11263-024-02157-wOnline publication date: 26-Jul-2024
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
AbbasiRad EKeyvanpour MTohidi N(2024)Co-clustering method for cold start issue in collaborative filtering movie recommender systemMultimedia Tools and Applications10.1007/s11042-024-20103-3Online publication date: 13-Sep-2024
https://doi.org/10.1007/s11042-024-20103-3
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
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Airen SAgrawal J(2024)Performance Improvement of Movie Recommender System Using Spectral Bi-clustering with Mahalabonis DistanceComputing, Internet of Things and Data Analytics10.1007/978-3-031-53717-2_31(324-333)Online publication date: 22-Feb-2024
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Yang ZLiu JLi TWu DYang SLiu HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)A Two-tier Shared Embedding Method for Review-based Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614770(2928-2938)Online publication date: 21-Oct-2023
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Qu LTang NZheng RNguyen QHuang ZShi YYin H(2023)Semi-decentralized Federated Ego Graph Learning for RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583337(339-348)Online publication date: 30-Apr-2023
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Dong YLi JSchnabel TChen HDuh WHuang HKato MMothe JPoblete B(2023)When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591785(942-952)Online publication date: 19-Jul-2023
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Airen SAgrawal J(2023)Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-ClusteringProcedia Computer Science10.1016/j.procs.2023.01.096218:C(1176-1183)Online publication date: 1-Jan-2023
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