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CLC number: TP301.6

On-line Access: 2013-09-05

Received: 2013-03-15

Revision Accepted: 2013-06-26

Crosschecked: 2013-08-07

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.9 P.711-721

http://doi.org/10.1631/jzus.CIIP1303


Enhancing recommender systems by incorporating social information


Author(s):  Li-wei Huang, Gui-sheng Chen, Yu-chao Liu, De-yi Li

Affiliation(s):  Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China; more

Corresponding email(s):   huangliwei.1985@gmail.com, lidy@cae.cn

Key Words:  Recommender system, Social information, Factor graph model


Li-wei Huang, Gui-sheng Chen, Yu-chao Liu, De-yi Li. Enhancing recommender systems by incorporating social information[J]. Journal of Zhejiang University Science C, 2013, 14(9): 711-721.

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Abstract: 
Although recommendation techniques have achieved distinct developments over the decades, the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper, based on a factor graph model, we formalize the problem in a semi-supervised probabilistic model, which can incorporate different user information, user relationships, and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets, Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore, a distributed learning algorithm is developed to scale up the approach to real large datasets.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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