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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (9): 711-721    DOI: 10.1631/jzus.CIIP1303
    
Enhancing recommender systems by incorporating social information
Li-wei Huang, Gui-sheng Chen, Yu-chao Liu, De-yi Li
Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China; Institute of Electronic System Engineering, Beijing 100039, China
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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.

Key wordsRecommender system      Social information      Factor graph model     
Received: 15 March 2013      Published: 05 September 2013
CLC:  TP301.6  
Cite this article:

Li-wei Huang, Gui-sheng Chen, Yu-chao Liu, De-yi Li. Enhancing recommender systems by incorporating social information. Front. Inform. Technol. Electron. Eng., 2013, 14(9): 711-721.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.CIIP1303     OR     http://www.zjujournals.com/xueshu/fitee/Y2013/V14/I9/711


Enhancing recommender systems by incorporating social information

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.

关键词: Recommender system,  Social information,  Factor graph model 
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