2013 FISMFactoredItemSimilarityModel

• (Kabbur et al., 2013) ⇒ Santosh Kabbur, Xia Ning, and George Karypis. (2013). “FISM: Factored Item Similarity Models for Top-N Recommender Systems.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487589

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+FISM%3A+Factored+Item+Similarity+Models+for+Top-N+Recommender+Systems
• http://dl.acm.org/citation.cfm?id=2487575.2487589&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; item similarity; recommender systems; sparse data; topn

Abstract

The effectiveness of existing top -N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 FISMFactoredItemSimilarityModel	George Karypis Santosh Kabbur Xia Ning			FISM: Factored Item Similarity Models for Top-N Recommender Systems				10.1145/2487575.2487589		2013