Sparse linear methods with side information for top-N recommendations

Xia Ning, George Karypis

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

81 Scopus citations

Abstract

The increasing amount of side information associated with the items in E-commerce applications has provided a very rich source of information that, once properly exploited and incorporated, can significantly improve the performance of the conventional recommender systems. This paper focuses on developing effective algorithms that utilize item side information for top-N recommender systems. A set of sparse linear methods with side information (SSLIM) is proposed, which involve a regularized optimization process to learn a sparse aggregation coefficient matrix based on both useritem purchase profiles and item side information. This aggregation coefficient matrix is used within an item-based recommendation framework to generate recommendations for the users. Our experimental results demonstrate that SSLIM outperforms other methods in effectively utilizing side information and achieving performance improvement.

Original languageEnglish (US)
Title of host publicationRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
Pages155-162
Number of pages8
DOIs
StatePublished - Oct 17 2012
Event6th ACM Conference on Recommender Systems, RecSys 2012 - Dublin, Ireland
Duration: Sep 9 2012Sep 13 2012

Publication series

NameRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems

Other

Other6th ACM Conference on Recommender Systems, RecSys 2012
Country/TerritoryIreland
CityDublin
Period9/9/129/13/12

Keywords

  • Recommender system
  • Side information
  • Sparse linear methods

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