Computer Science ›› 2017, Vol. 44 ›› Issue (3): 231-236.doi: 10.11896/j.issn.1002-137X.2017.03.048

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Research on Method of Group Recommendation for Fusion of Hidden Features

LIU Yi, ZHONG Xian and LI Lin   

  • Online:2018-11-13 Published:2018-11-13

Abstract: As the most successful mainstream recommendation method,singular value decomposition (SVD) algorithm builds the model from known huge data and uses the matrix decomposition dimension reduction to get effective information,and non negative matrix factorization (NMF) uses the decomposition of nonnegative matrix elements to explain the meanings of characteristics.These two kinds of successful methods are based on matrix decomposition of explicit feedback information,and obtain the user’s preference information.However,they cannot accurately reflect the true preferences of the users only according to user’s explicit feedback.To solve the problem,this paper put forward an improved method for the two models,integrating the hidden features and weight calculation method based on hidden features into the classical matrix decomposition algorithm,the hidden features can perfect the information of user’s prefe-rences.Weight calculation method based on hidden features can judge the group characteristics and give the appropriate weight to users,which improve the recommendation accuracy.The method was tested on the Tencent micro blog data set in KDD Cup 2012 Track1.The results show that from the experimental standard of the MAE and the precision,the fusion method is better than SVD and NMF on this data set,and significantly improves the recommendation perfor-mance.

Key words: Group recommendation,Hidden features,Weights of the group,MAE

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