SLIM: Sparse LInear Methods for top-N recommender systems

Xia Ning, George Karypis

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

328 Scopus citations

Abstract

This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse LInear Method (SLIM) is proposed, which generates top- N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an ℓ 1-norm and ℓ 2-norm regularized optimization problem. W is demonstrated to produce highquality recommendations and its sparsity allows SLIM to generate recommendations very fast. A comprehensive set of experiments is conducted by comparing the SLIM method and other state-of-the-art top-N recommendation methods. The experiments show that SLIM achieves significant improvements both in run time performance and recommendation quality over the best existing methods.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages497-506
Number of pages10
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/14/11

Keywords

  • Sparse LInear Methods
  • Top-N recommender systems
  • ℓ -norm regularization

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