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	A modified fuzzy C-means algorithm for collaborative filtering
	Wu, Jinlong ; Li, Tiejun
	2008
英文摘要	Two major challenges for collaborative filtering problems are scalability and sparseness. Some powerful approaches have been developed to resolve these challenges. Two of them are Matrix Factorization (MF) and Fuzzy C-means (FCM). In this paper we combine the ideas of MF and FCM, and propose a new clustering model - - Modified Fuzzy C-means (MFCM). MFCM has better interpretability than MF, and better accuracy than FCM. MFCM also supplies a new perspective on MF models. Two new algorithms are developed to solve this new model. They are applied to the Netflix Prize data set and acquire comparable accuracy with that of MF. Copyright 2008 ACM.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/315533]
专题	数学科学学院
推荐引用方式 GB/T 7714	Wu, Jinlong,Li, Tiejun. A modified fuzzy C-means algorithm for collaborative filtering. 2008-01-01.

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