Cross-Domain Item Recommendation Based on User Similarity

Zhenzhen Xu1, Huizhen Jiang1, Xiangjie Kong1, Jialiang Kang1, Wei Wang1 and Feng Xia1

  1. School of Software, Dalian University of Technology
    Dalian 116620, China,,,,,


Cross-domain recommender systems adopt multiple methods to build relations from source domain to target domain in order to alleviate problems of cold start and sparsity, and improve the performance of recommendations. The majority of traditional methods tend to associate users and items, which neglected the strong influence of friend relation on the recommendation. In this paper, we propose a cross-domain item recommendation model called CRUS based on user similarity, which firstly introduces the trust relation among friends into cross-domain recommendation. Despite friends usually tend to have similar interests in some domains, they share differences either. Considering this, we define all the similar users with the target user as Similar Friends. By modifying the transfer matrix in the random walk, friends sharing similar interests are highlighted. Extensive experiments on Yelp data set show CRUS outperforms the baseline methods on MAE and RMSE.

Key words

cross domain recommendation, trust relation, user similarity, rating prediction, random walk

Digital Object Identifier (DOI)

Publication information

Volume 13, Issue 2 (June 2016)
Year of Publication: 2016
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Xu, Z., Jiang, H., Kong, X., Kang, J., Wang, W., Xia, F.: Cross-Domain Item Recommendation Based on User Similarity. Computer Science and Information Systems, Vol. 13, No. 2, 359–373. (2016),