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Social Recommendation with Biased Regularization |
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PP: 2637-2644 |
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Author(s) |
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Xiang Hu,
Wendong Wang,
Xiangyang Gong,
Bai Wang,
Xirong Que,
Hongke Xia,
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Abstract |
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| Although recommendation systems are the most important methods for resolving the ”information overload” problem,
majority of them are beset by their inherent flaws.With the recent emergence of online social networks, the increasing social information
has offered opportunities to relieve these flaws. In this paper, a new matrix factorization based social recommendation method is
proposed, in which social relations and the rating habit are integrated into the objective function via appending additional penalty
term and bias term to classic probabilistic matrix factorization model. In order to involve more social information into traditional
recommendation system, the proposed method adopt the social similarity rather than interest similarity to measure the closeness degree
between users. Experiment shows that our method has got better performances than homologous methods. |
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