research-article

Improve collaborative filtering through bordered block diagonal form matrices

Authors:

Yongfeng Zhang,

Shaoping MaAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 313 - 322

https://doi.org/10.1145/2484028.2484101

Published: 28 July 2013 Publication History

Abstract

Collaborative Filtering-based recommendation algorithms have achieved widespread success on the Web, but little work has been performed to investigate appropriate user-item relationship structures of rating matrices. This paper presents a novel and general collaborative filtering framework based on (Approximate) Bordered Block Diagonal Form structure of user-item rating matrices. We show formally that matrices in (A)BBDF structures correspond to community detection on the corresponding bipartite graphs, and they reveal relationships among users and items intuitionally in recommendation tasks. By this framework, general and special interests of a user are distinguished, which helps to improve prediction accuracy in collaborative filtering tasks. Experimental results on four real-world datasets, including the Yahoo! Music dataset, which is currently the largest, show that the proposed framework helps many traditional collaborative filtering algorithms, such as User-based, Item-based, SVD and NMF approaches, to make more accurate rating predictions. Moreover, by leveraging smaller and denser submatrices to make predictions, this framework contributes to the scalability of recommender systems.

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Cited By

Kang JYang LSun YLin YZhang SLin H(2024)P-Reader: A Clue-Inspired Model for Machine Reading ComprehensionCognitive Computing – ICCC 202310.1007/978-3-031-51671-9_2(19-33)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-3-031-51671-9_2
Ifada NCahyaningrum ERachman F(2022)The Implementation of Hybrid Semantic Ontology-based Model on Movie Recommendation System2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)10.1109/CENIM56801.2022.10037277(1-7)Online publication date: 22-Nov-2022
https://doi.org/10.1109/CENIM56801.2022.10037277
Xie LHu ZCai XZhang WChen J(2021)Explainable recommendation based on knowledge graph and multi-objective optimizationComplex & Intelligent Systems10.1007/s40747-021-00315-y7:3(1241-1252)Online publication date: 6-Mar-2021
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Index Terms

Improve collaborative filtering through bordered block diagonal form matrices
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 Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 ACM.

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Published: 28 July 2013

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SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.1007/978-3-031-51671-9_2
Ifada NCahyaningrum ERachman F(2022)The Implementation of Hybrid Semantic Ontology-based Model on Movie Recommendation System2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)10.1109/CENIM56801.2022.10037277(1-7)Online publication date: 22-Nov-2022
https://doi.org/10.1109/CENIM56801.2022.10037277
Xie LHu ZCai XZhang WChen J(2021)Explainable recommendation based on knowledge graph and multi-objective optimizationComplex & Intelligent Systems10.1007/s40747-021-00315-y7:3(1241-1252)Online publication date: 6-Mar-2021
https://doi.org/10.1007/s40747-021-00315-y
Cao KLiu YZhang H(2020)Improving the Cold Start Problem in Music Recommender SystemsJournal of Physics: Conference Series10.1088/1742-6596/1651/1/0120671651:1(012067)Online publication date: 1-Nov-2020
https://doi.org/10.1088/1742-6596/1651/1/012067
Liu HJing LQian YYu J(2019)Adaptive Local Low-rank Matrix Approximation for RecommendationACM Transactions on Information Systems10.1145/336048837:4(1-34)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3360488
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Lin XZhang MLiu YMa S(2019)Enhancing Personalized Recommendation by Implicit Preference Communities ModelingACM Transactions on Information Systems10.1145/335259237:4(1-32)Online publication date: 26-Nov-2019
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Benham RMackenzie JMoffat ACulpepper J(2019)Boosting Search Performance Using Query VariationsACM Transactions on Information Systems10.1145/334500137:4(1-25)Online publication date: 4-Oct-2019
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Liu CLu TWang XCheng ZSun JHoi SPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Compositional Coding for Collaborative FilteringProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331206(145-154)Online publication date: 18-Jul-2019
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