Title :
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
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;
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
DOI :
10.1109/ICNIDC.2009.5360810
