Pagerank-based collaborative filtering recommendation

Authors:
Feng Jiang;Zhijun Wang
Affiliations:
College of Civil Engineering, Chongqing University, Chongqing, China and Construction Engineering Department, Chongqing Technology and Business Institute, Chongqing, China;College of Civil Engineering, Chongqing University, Chongqing, China
Venue:
ICICA'10 Proceedings of the First international conference on Information computing and applications
Year:
2010

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Abstract

Item-based collaborative filtering (CF) is one of the most popular recommendation approaches. A weakness of current item-based CF is all users have the same weight in computing item relationships. In order to solve the problem, we incorporate userrank as weight of a user based on PageRank into item similarities computing. In this paper, a data model for userrank calculation, a user ranking approach, and a userrank-based item-item similarities computing approach are proposed. Finally, we experimentally evaluate our approach for recommendation and compare it to traditional item-based Adjusted Cosine recommendation approach.