Sparse linear methods with side information for Top-N recommendations

Authors:
Xia Ning;George Karypis
Affiliations:
University of Minnesota, Twin Ciities, Minneapolis, MN, USA;University of Minnesota, Twin Cities, Minneapolis, MN, USA
Venue:
Proceedings of the 21st international conference companion on World Wide Web
Year:
2012

Citing 5
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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.