Computer Science ›› 2018, Vol. 45 ›› Issue (4): 215-219.doi: 10.11896/j.issn.1002-137X.2018.04.036

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Study on Matrix Factorization Recommendation Algorithm Based on User Clustering and Mobile Context

WEN Jun-hao, SUN Guang-hui and LI Shun   

  • Online:2018-04-15 Published:2018-05-11

Abstract: With the rapid development of mobile Internet technology,more and more individuals use mobile devices to acquire information and services,which makes information overload problem more and more serious.Aiming at the puzzle resulted from data sparsity,insufficient contextual information and ignoring context similarity measurement,this paper proposesd a method of matrix factorization recommendation algorithm based on user clustering and mobile context(UCMC-MF) to predict user ratings and make recommendation.Firstly,the method clusters similar user by way of k-means,then finds similar contexts in each cluster,and searches users who are similar to the target user in preferences and context.Finally,experimental results on real datasets demonstrate that the proposed algorithm can effectively improve the accuracy of prediction.

Key words: Clustering,Context information,Matrix factorization,Recommendation

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