SLIM: Sparse Linear Methods for Top-N Recommender Systems


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 `1-norm and `2-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.

DOI: 10.1109/ICDM.2011.134
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@article{Ning2011SLIMSL, title={SLIM: Sparse Linear Methods for Top-N Recommender Systems}, author={Xia Ning and George Karypis}, journal={2011 IEEE 11th International Conference on Data Mining}, year={2011}, pages={497-506} }