Computer Science ›› 2016, Vol. 43 ›› Issue (9): 227-231.doi: 10.11896/j.issn.1002-137X.2016.09.045

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Collaborative Filtering Algorithm Integrating Multiple User Behaviors

GAO Shan, LIU Wei, CUI Yong, ZHANG Qian and WANG Zong-min   

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

Abstract: Collaborative filtering is one of the most successful techniques among personalized recommender systems,and is widely used in the field of e-commerce,social networks etc.Due to the complexity and diversity of the personal health behaviors,it causes low accuracy of the recommendation algorithms when applied to personalized medicine recommendation.To deal with this problem,a new collaborative filtering algorithm integrating multiple user behaviors was proposed.The weighting factor and the overlap-dependency are introduced in classical similarity computing,and they can measure the effects of different user behaviors on the recommendation quality.Experiments on the Top-md dataset show that the new algorithm can fully express the user’s preferences and wishes for medical treatment,and can effectively improve the quality of personalized medicine recommender systems.

Key words: Recommender systems,Collaborative filtering,Overlap-dependency,Multiple user behaviors,Weighting factor

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