Computer Science ›› 2019, Vol. 46 ›› Issue (8): 28-34.doi: 10.11896/j.issn.1002-137X.2019.08.005

• Big Data & Data Science • Previous Articles     Next Articles

Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation

DENG Cun-bin1,2, YU Hui-qun1, FAN Gui-sheng1   

  1. Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)1
    (Shanghai Key Laboratory of Computer Software Evaluating and Testing,Shanghai 201112,China)2
  • Received:2018-07-08 Online:2019-08-15 Published:2019-08-15

Abstract: In the era of information explosion,the recommendation system plays an enormous role in reducing information overload.At present,the recommendation system generally uses the traditional collaborative filtering algorithm to learn the hidden vector in the user-item behavior matrix,but it has the problem of data sparseness and cold start,and does not consider the customer preferences and the popularity dynamics of items.This greatly limits the accuracy of the recommendation system.Some scholars have used the deep learning model to learn the features of the auxiliary information to enrich the features of the collaborative filtering algorithm,and achieved certain results,which does not fully solve all the problems.This paper took film recommendation as the research object,and proposed a recommendation algorithm that combines dynamic collaborative filtering and deep learning.Firstly,the dynamic collaborative filtering algorithm incorporates temporal features.Secondly,it uses deep learning model to learn user and movie feature information to form the hidden vector of user features and movie features in high-dimensional latent space.Finally,it is integrated into the dynamic collaborative filtering algorithm.Extensive experiments on MovieLens datasets show that the proposed method improves the accuracy of film score prediction

Key words: Movie recommendation, Hidden vector, Deep learning, Dynamic collaborative filtering

CLC Number: 

  • TP311
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