Sparse Linear Methods with Side Information for top-N recommendations

Xia Ning, George Karypis

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

3 Scopus citations

Abstract

This paper focuses on developing effective algorithms that utilize side information for top-N recommender systems. A set of Sparse Linear Methods with Side information (SSLIM) is proposed, that utilize a regularized optimization process to learn a sparse item-to-item coefficient matrix based on historical user-item purchase profiles and side information associated with the items. This coefficient matrix is used within an item-based recommendation framework to generate a size-N ranked list of items for a user. Our experimental results demonstrate that SSLIM outperforms other methods in effectively utilizing side information and achieving performance improvement. Copyright is held by the author/owner(s).

Original languageEnglish (US)
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Pages581-582
Number of pages2
DOIs
StatePublished - May 21 2012
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: Apr 16 2012Apr 20 2012

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Other

Other21st Annual Conference on World Wide Web, WWW'12
Country/TerritoryFrance
CityLyon
Period4/16/124/20/12

Keywords

  • Recommender systems
  • Side Information
  • Sparse Linear Methods

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