Personalized Recommendation Combining User Interest and Social Circle

被引:257
作者
Qian, Xueming [1 ]
Feng, He [1 ]
Zhao, Guoshuai [1 ]
Mei, Tao [2 ]
机构
[1] Xi An Jiao Tong Univ, SMILES LAB, CN-710049 Xian, Peoples R China
[2] Microsoft Res, CN-10080 Beijing, Peoples R China
关键词
Interpersonal influence; personal interest; recommender system; social networks; GENERATION; PHOTOS;
D O I
10.1109/TKDE.2013.168
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches.
引用
收藏
页码:1763 / 1777
页数:15
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