Sparse linear methods with side information for Top-N recommendations
Proceedings of the 21st international conference companion on World Wide Web
Sparse linear methods with side information for top-n recommendations
Proceedings of the sixth ACM conference on Recommender systems
Learning User Preference Patterns for Top-N Recommendations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
FISM: factored item similarity models for top-N recommender systems
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
AdaM: adaptive-maximum imputation for neighborhood-based collaborative filtering
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Top-N recommendations from implicit feedback leveraging linked open data
Proceedings of the 7th ACM conference on Recommender systems
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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.