@inproceedings{2d67af1b57b945daa124aa96f63dcbdd,
title = "SLIM: Sparse LInear Methods for top-N recommender systems",
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.",
keywords = "Sparse LInear Methods, Top-N recommender systems, ℓ -norm regularization",
author = "Xia Ning and George Karypis",
year = "2011",
month = dec,
day = "1",
doi = "10.1109/ICDM.2011.134",
language = "English (US)",
isbn = "9780769544083",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
pages = "497--506",
booktitle = "Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011",
note = "11th IEEE International Conference on Data Mining, ICDM 2011 ; Conference date: 11-12-2011 Through 14-12-2011",
}