Research on the Personal Recommendation Algorithm Based on Grey Relationship

With the rapid development of Internet technology, personal recommendation systems have become effective in the search for massive data on the user's most important tool useful information. Personal recommendation algorithm is the core of the recommendation system and is paid more attention by many researchers. Collaborative filtering algorithm is proposed firstly and is used widely. This paper analyzes the traditional collaborative filtering algorithms firstly and presents some shortcomings in it. Through the introduction of the gray relational coefficient, this paper presents the calculation method of grey relational similarity for personal recommendation, and analyzes its properties. By using Movie-Lens data set, the paper compares the advantages and disadvantages of the two algorithms. The numerical results show that the grey personal recommendation algorithm greatly improved the accuracy of recommendation system, At last, some conclusions are presented in the paper.

[1]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[2]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[3]  Alfred Kobsa.  User Modeling and User-Adapted Interaction , 2005, User Modeling and User-Adapted Interaction.

[4]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[5]  Jonathan L. Herlocker,et al.  Clustering items for collaborative filtering , 1999 .

[6]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[7]  Xiao-Dong Li,et al.  Comparison Study of Internet Recommendation System: Comparison Study of Internet Recommendation System , 2009 .

[8]  Benjamin M. Marlin,et al.  Collaborative Filtering: A Machine Learning Perspective , 2004 .

[9]  Neil J. Hurley,et al.  Collaborative recommendation: A robustness analysis , 2004, TOIT.

[10]  Sergio A. Alvarez,et al.  Collaborative Recommendation via Adaptive Association Rule Mining , 2000 .

[11]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[12]  Bong-Jin Yum,et al.  Collaborative filtering based on iterative principal component analysis , 2005, Expert Syst. Appl..

[13]  Wu Xiao,et al.  Comparison Study of Internet Recommendation System , 2009 .

[14]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[15]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.