2011 SLIMSparseLinearMethodsforTopNR

• (Ning & Karypis, 2011) ⇒ Xia Ning, and George Karypis. (2011). “SLIM: Sparse Linear Methods for Top-N Recommender Systems.” In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining. ISBN:978-0-7695-4408-3 doi:10.1109/ICDM.2011.134

Subject Headings: SLIM Algorithm.

Notes

• Slides http://www-users.cs.umn.edu/~xning/slides/ICDM2011_slides.pdf

Cited By

• http://scholar.google.com/scholar?q=%222011%22+SLIM%3A+Sparse+Linear+Methods+for+Top-N+Recommender+Systems
• http://dl.acm.org/citation.cfm?id=2117684.2118303&preflayout=flat#citedby

Quotes

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 `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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 SLIMSparseLinearMethodsforTopNR	George Karypis Xia Ning			SLIM: Sparse Linear Methods for Top-N Recommender Systems				10.1109/ICDM.2011.134		2011