Computer Science ›› 2017, Vol. 44 ›› Issue (12): 52-57.doi: 10.11896/j.issn.1002-137X.2017.12.010

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Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and Item Correlation

ZANG Xue-feng, LIU Tian-qi, SUN Xiao-xin, FENG Guo-zhong and ZHANG Bang-zuo   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to satisfy the information needs of users in the big data era,the personalized recommender system has been widely used.Collaborative filtering is a simple and effective recommendation algorithm.However,most traditional similarity methods only compute the similarity based on the users’ co-rated scores.In addition,they are not very suitable in sparse data environment.This paper proposed a new similarity method based on Bhattacharyya coefficient.It uses all users’ rating information for items,which can not only obtain similar interest feature of users through the user’srating behavior,but also obtain the correlation between the items that the users have rated.Meanwhile,the new me-thod also takes into account each user’s rating preference,since different users have different rating habits.Considering more relevant factor about user similarity,more appropriate neighborhood can be selected for the target users,efficiently improving the recommendations.With experiments on two real data sets,the results show that our method outperforms the other state-of-the-art similarity metrics.

Key words: Cllaborative filtering,Bhattacharyya coefficient,Item correlation,User preference

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