Computer Science ›› 2019, Vol. 46 ›› Issue (9): 216-222.doi: 10.11896/j.issn.1002-137X.2019.09.032

• Artificial Intelligence • Previous Articles     Next Articles

Collaborative Filtering Recommendation Algorithm Mixing LDA Model and List-wise Model

WANG Han, XIA Hong-bin   

  1. (School of Digital Media,Jiangnan University,Wuxi,Jiangsu 214122,China);
    (Key Laboratory of Media Design and Software Technology of Jiangsu Province,Jiangnan University,Wuxi,Jiangsu 214122,China)
  • Received:2018-07-12 Online:2019-09-15 Published:2019-09-02

Abstract: Rranking-oriented collaborative filtering is affected by the sparsity of data,which leads to the inaccuracy of recommendations.This paper proposed a hybrid ranking-oriented collaborative filtering algorithm based on LDA topic model and list-wise model.The algorithm uses the LDA topic model to model the user-item ratings matrix,and obtains the potential low-dimensional topic vector of the user,then measures the similarity between users with the topic vector.Next,the list-wise learning function is used to directly predict the total order of items that satisfies the users preference.The experimental results on the two real datasets of Movielens and EachMovie show that the algorithm can avoid the inaccuracy of similarity calculation between users caused by too little common score information,and at the same time reflect the superiority of learning to rank.It can effectively alleviate the effect of data sparsity and improve the accuracy of recommendation.

Key words: Collaborative filtering, Learning to rank, List-wise model, LDA topic model

CLC Number: 

  • TP301
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