Geodesic
Cross-Domain Item Recommendation Based on User Similarity
Computer Science and Information Systems, Tome 13 (2016) no. 2.

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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.
Keywords: cross domain recommendation, trust relation, user similarity, rating prediction, random walk 
@article{CSIS_2016_13_2_a4,
     author = {Zhenzhen Xu and Huizhen Jiang and Xiangjie Kong and Jialiang Kang and Wei Wang and Feng Xia},
     title = {Cross-Domain {Item} {Recommendation} {Based} on {User} {Similarity}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {13},
     number = {2},
     year = {2016},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2016_13_2_a4/}
}
TY  - JOUR
AU  - Zhenzhen Xu
AU  - Huizhen Jiang
AU  - Xiangjie Kong
AU  - Jialiang Kang
AU  - Wei Wang
AU  - Feng Xia
TI  - Cross-Domain Item Recommendation Based on User Similarity
JO  - Computer Science and Information Systems
PY  - 2016
VL  - 13
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2016_13_2_a4/
ID  - CSIS_2016_13_2_a4
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%A Xiangjie Kong
%A Jialiang Kang
%A Wei Wang
%A Feng Xia
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%J Computer Science and Information Systems
%D 2016
%V 13
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2016_13_2_a4/
%F CSIS_2016_13_2_a4
Zhenzhen Xu; Huizhen Jiang; Xiangjie Kong; Jialiang Kang; Wei Wang; Feng Xia. Cross-Domain Item Recommendation Based on User Similarity. Computer Science and Information Systems, Tome 13 (2016) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2016_13_2_a4/