SLIM: Sparse Linear Methods for Top-N Recommender Systems
|Xia Ning and George Karypis|
|ICDM , 2011|
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 L1-norm and L2-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.
|Research topics: Collaborative filtering | Data mining | SLIM|