JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2015, Vol. 50 ›› Issue (09): 29-35.doi: 10.6040/j.issn.1671-9352.3.2014.232

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Hybrid recommendation by combining network-based algorithm and user preference

ZHANG Xin-meng1,2, JIANG Sheng-yi1,2, ZHANG Qian-sheng2,3, XIE Bo-lin1,2, LI Xia2   

  1. 1. Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China;
    2. Social Science Key Laboratory of Language Engineering and Computing of Guangdong Province, Guangzhou 510006, Guangdong, China;
    3. School of Finance, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China
  • Received:2015-03-03 Revised:2015-07-22 Online:2015-09-20 Published:2015-09-26

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Abstract: Recommendation algorithms based on heat conduction or mass diffusion first obtain the relationship between objects according to network structure, then predict the user's favorite objects based on these relationships, but these algorithms ignore user's preference. In order to overcome this defect, the TF-IDF approach was used to construct user's preference according to the tags contained in the objects selected by user, and the mean of preference of object's tags was taken as the preference of the object, then a hybrid recommendation model was proposed by combining network-based algorithm and the user preference model. The benchmark datasets, MovieLens, was used to evaluate our algorithm, and the experimental results demonstrate that hybrid algorithm can significantly improve accuracy, diversification and personalization of recommendations.

Key words: TF-IDF, network-based recommendation, personalized recommendation, tag

CLC Number: 

  • TP393
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