A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD)

被引:12
|
作者
Zhou, Xun [1 ]
He, Jing [2 ]
Huang, Guangyan [2 ]
Zhang, Yanchun [1 ,2 ]
机构
[1] Chinese Acad Sci, Grad Univ, GUCAS VU Joint Lab Social Comp & E Hlth Res, Beijing, Peoples R China
[2] Victoria Univ, Ctr Appl Informat, Sch Engn & Sci, Melbourne, Vic, Australia
基金
中国国家自然科学基金;
关键词
singular value decomposition; personalization; recommendation system; experimental evaluation; CUSTOMER LIFETIME VALUE; MATRIX; RANK;
D O I
10.1109/WI-IAT.2012.225
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Personalized recommendation is, according to the user's interest characteristics and purchasing behavior, to recommend information and goods to users in which they may be interested. With the rapid development of Internet technology, we have entered the era of information explosion, where huge amounts of information are presented at the same time. On one hand, it is difficult for the user to discover information in which he is most interested; on the other hand, general users experience difficult in obtaining information which very few people browse. In order to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm based on approximating the singular value decomposition (SVD) in this paper. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on the MovieLens dataset, and show that our method has the best prediction quality.
引用
收藏
页码:458 / 464
页数:7
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