short-paper

A general collaborative filtering framework based on matrix bordered block diagonal forms

Authors:

Yongfeng Zhang,

Shaoping MaAuthors Info & Claims

HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media

Pages 219 - 224

https://doi.org/10.1145/2481492.2481520

Published: 01 May 2013 Publication History

Abstract

Recommender systems based on Collaborative Filtering (CF) techniques have achieved great success in e-commerce, social networks and various other applications on the Web. However, problems such as data sparsity and scalability are still important issues to be investigated in CF algorithms. In this paper, we present a novel CF framework that is based on Bordered Block Diagonal Form (BBDF) matrices attempting to meet the challenges of data sparsity and scalability. In this framework, general and special interests of users are distinguished, which helps to improve prediction accuracy in collaborative filtering tasks. Experimental results on four real-world datasets show that the proposed framework helps many traditional CF algorithms 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

Zhang YChen XZhang YChen XDiaz FShah CSuel TCastells PJones RSakai T(2021)CSR 2021: The 1st International Workshop on Causality in Search and RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462817(2677-2680)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462817
Zhang YZhang MZhang YLiu YMa SLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Understanding the SparsityProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661976(1189-1198)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661976
Zhang YKobsa AZhou MEster MKoren Y(2014)Browser-oriented universal cross-site recommendation and explanation based on user browsing logsProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2653367(433-436)Online publication date: 6-Oct-2014
https://dl.acm.org/doi/10.1145/2645710.2653367

Index Terms

A general collaborative filtering framework based on matrix bordered block diagonal forms
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

HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media

May 2013

275 pages

ISBN:9781450319676

DOI:10.1145/2481492

General Chair:
Gerd Stumme
University of Kassel, Germany
,
Program Chair:
Andreas Hotho
University of Würzburg, Germany

Copyright © 2013 ACM.

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

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New York, NY, United States

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Published: 01 May 2013

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HT '13: 24th ACM Conference on Hypertext and Social Media

May 1 - 3, 2013

Paris, France

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HT '13 Paper Acceptance Rate 16 of 96 submissions, 17%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Zhang YChen XZhang YChen XDiaz FShah CSuel TCastells PJones RSakai T(2021)CSR 2021: The 1st International Workshop on Causality in Search and RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462817(2677-2680)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462817
Zhang YZhang MZhang YLiu YMa SLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Understanding the SparsityProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661976(1189-1198)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661976
Zhang YKobsa AZhou MEster MKoren Y(2014)Browser-oriented universal cross-site recommendation and explanation based on user browsing logsProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2653367(433-436)Online publication date: 6-Oct-2014
https://dl.acm.org/doi/10.1145/2645710.2653367

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