Userrank for item-based collaborative filtering recommendation

Authors:
Min Gao;Zhongfu Wu;Feng Jiang
Affiliations:
School of Software Engineering, Chongqing University, Chongqing 400044, China;College of Computer Science, Chongqing University, Chongqing 400044, China;Chongqing Radio and TV University, Chongqing 400052, China
Venue:
Information Processing Letters
Year:
2011

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Abstract

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.