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	Collaborative factorization for recommender systems
	Fant, Chaosheng ; Lan, Yanyan ; Guo, Jiafeng ; Lint, Zuoquan ; Cheng, Xueqi
	2013
英文摘要	Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real rec-ommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most N-N methods leverage the neighbors' behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods. Copyright ? 2013 ACM.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/315505]
专题	数学科学学院
推荐引用方式 GB/T 7714	Fant, Chaosheng,Lan, Yanyan,Guo, Jiafeng,et al. Collaborative factorization for recommender systems. 2013-01-01.

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