Computer Science ›› 2016, Vol. 43 ›› Issue (12): 158-162.doi: 10.11896/j.issn.1002-137X.2016.12.028

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Item-based Collaborative Filtering Algorithm Integrating User Activity and Item Popularity

WANG Jin-kun, JIANG Yuan-chun, SUN Jian-shan and SUN Chun-hua   

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

Abstract: Item correlation computation is the most critical component in item- based collaborative filtering algorithm.The traditional correlation computation scheme can be challenged by the sparse data set and the situation of recommending unpopular products.In this paper,a novel item- based collaborative filtering algorithm that incorporates the activity of users and popularity of items was proposed.The proposed computation scheme decreases the correlation between items using the activity of users and popularity of items in those rating records where only one item is rated.In this way,the unpopular products can be recommended to users in the sparse data.Experimental evaluation shows that the diversity and novelty of the recommendation list can be improved while maintaining the prediction accuracy.

Key words: Personalized recommendation,Correlation computation,Collaborative filtering,Activity of users,Popularity of items

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