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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularized optimization problem. W is demonstrated to produce high quality 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.

Keywords

Recommender systemComputer scienceSet (abstract data type)Norm (philosophy)Sparse matrixMatrix (chemical analysis)Quality (philosophy)Information retrievalData miningProgramming language

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Publication Info

Year
2011
Type
article
Pages
497-506
Citations
721
Access
Closed

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Xia Ning, George Karypis (2011). SLIM: Sparse Linear Methods for Top-N Recommender Systems. , 497-506. https://doi.org/10.1109/icdm.2011.134

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DOI
10.1109/icdm.2011.134