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

Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationHT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media
Pages219-224
Number of pages6
DOIs
StatePublished - 2013
Externally publishedYes
Event24th ACM Conference on Hypertext and Social Media, HT 2013 - Paris, France
Duration: May 1 2013May 3 2013

Publication series

NameHT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media

Other

Other24th ACM Conference on Hypertext and Social Media, HT 2013
Country/TerritoryFrance
CityParis
Period5/1/135/3/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Bordered block diagonal form
  • Collaborative filtering
  • Graph partitioning

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