Localized matrix factorization for recommendation based on matrix Block Diagonal Forms

Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, Shi Feng

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

49 Scopus citations

Abstract

Matrix factorization on user-item rating matrices has achieved significant success in collaborative filtering based recommendation tasks. However, it also encounters the problems of data sparsity and scalability when applied in real-world recommender systems. In this paper, we present the Localized Matrix Factorization (LMF) framework, which attempts to meet the challenges of sparsity and scalability by factorizing Block Diagonal Form (BDF) matrices. In the LMF framework, a large sparse matrix is first transformed into Recursive Bordered Block Diagonal Form (RBBDF), which is an intuitionally interpretable structure for user-item rating matrices. Smaller and denser submatrices are then extracted from this RBBDF matrix to construct a BDF matrix for more effective collaborative prediction. We show formally that the LMF framework is suitable for matrix factorization and that any decomposable matrix factorization algorithm can be integrated into this framework. It has the potential to improve prediction accuracy by factorizing smaller and denser submatrices independently, which is also suitable for parallelization and contributes to system scalability at the same time. Experimental results based on a number of realworld public-access benchmarks show the effectiveness and efficiency of the proposed LMF framework. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish (US)
Title of host publicationWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages1511-1520
Number of pages10
ISBN (Print)9781450320351
DOIs
StatePublished - 2013
Externally publishedYes
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: May 13 2013May 17 2013

Publication series

NameWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Other

Other22nd International Conference on World Wide Web, WWW 2013
Country/TerritoryBrazil
CityRio de Janeiro
Period5/13/135/17/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Block Diagonal Form
  • Collaborative Filtering
  • Graph Partitioning
  • Matrix Factorization

Fingerprint

Dive into the research topics of 'Localized matrix factorization for recommendation based on matrix Block Diagonal Forms'. Together they form a unique fingerprint.

Cite this