Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (04): 45-50.doi: 10.12052/gdutxb.170140

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A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information

Peng Jia-en1, Deng Xiu-qin1, Liu Tai-heng1, Liu Fu-chun2, Li Wen-zhou1   

  1. 1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China;
    2. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2017-10-10 Online:2018-07-09 Published:2018-05-24

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Abstract: In order to improve the recommendation performance of latent factor model under the circumstance of data sparseness, a latent factor model with the social regularization and the tag regularization is proposed. According to the user's social network and the item's tag information, the regularization depicting the profiles of the user and the item is designed, and the user rating preferences calculated by using user's history rating of items. These three items are introduced into the objective function of the matrix decomposition to further constrain the objective function. Finally, the gradient descent method is used to optimize the model parameters and get the recommendation result. To verify the efficacy of the proposed method, the model is tested by the Last.fm data set, and the experimental results show that the recommendation algorithm proposed in this study has a better recommendation performance compared with other traditional recommendation algorithms.

Key words: latent factor model, social network, tag information, recommendation algorithm

CLC Number: 

  • TP312
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