An improved collaborative filtering recommendation algorithm based on factor of credit

Author

Tong, Haiwei ; Lv, Tingjie ; Huang, Pei

Author_Institution

Sch. of Econ. & Manage., Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2009

fDate

6-8 Nov. 2009

Firstpage

424

Lastpage

429

Abstract

Traditional collaborative filtering algorithm is a weighted average prediction algorithm based on nearest neighbors´ ratings. Besides similarity between users, trust and credit are also parameters to affect recommendation. This paper proposes a computational model of credit factor and then a collaborative filtering algorithm based on it. This model is based on trust factor and takes credit model as the basic elements. This proposed algorithm further improves the validity and accuracy of the recommendation.

Keywords

groupware; recommender systems; collaborative filtering recommendation algorithm; credit factor; nearest neighbors ratings; trust factor; user similarity; weighted average prediction algorithm; Active filters; Assembly; Collaboration; Computational modeling; Databases; Economic forecasting; Filtering algorithms; Nearest neighbor searches; Neural networks; Prediction algorithms; Collaborative Filtering; Credit; Nearest Neighbor; Similarity; Trust;

fLanguage

English

Publisher

ieee

Conference_Titel

Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4898-2

Electronic_ISBN

978-1-4244-4900-6

Type

conf

DOI

10.1109/ICNIDC.2009.5360810

Filename

5360810