Computer Science ›› 2017, Vol. 44 ›› Issue (3): 247-253.doi: 10.11896/j.issn.1002-137X.2017.03.051

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Collaborative Filtering Recommendation Based on Item Splitting

HE Ming, LIU Yi, CHANG Meng-meng and WU Xiao-fei   

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

Abstract: Context-aware recommendation system is an effective way to improve the recommendation accuracy and user satisfaction by using context information.In this paper,an efficient context-item splitting approach for context-aware recommendation was proposed.Firstly,the items are divided according to the item split criterion.Secondly,the clustering is carried out through the context dimension based on the splitting results.Thirdly,the collaborative filtering re-commendation algorithm is used to predict the unknown ratings.Finally,simulation experiments are conducted on the LDOS-CoMoDa data set for different splitting criteria.The experimental results demonstrate that this method can effectively improve the accuracy of the recommendation and achieve the goal of improving the quality of recommendation.

Key words: Context-aware recommendation,Item-splitting context-aware approaches,Collaborative recommendation,Cluster

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