TY - GEN
T1 - Considering rating as probability distribution of attitude in recommender system
AU - Zhao, Xiangyu
AU - Niu, Zhendong
AU - Wang, Wentao
AU - Niu, Ke
AU - Yuan, Wu
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Recommender systems play increasingly significant roles in solving the information explosion problem. Generally, the user ratings are treated as ground truth of their tastes, and used as index for later predict unknown ratings. However, researchers have found that users are inconsistent in giving their feedbacks, which can be considered as rating noise. Some researchers focus on improving recommendation quality by de-noising user feedbacks. In this paper, we try to improve recommendation quality in a different way. The rating inconsistency is considered as an inherent characteristic of user feedbacks. User rating is described by the probability distribution of user attitude instead of the exact attitude towards the current item. According to it, we propose a recommendation approach based on conventional user-based collaborative filtering using the Manhattan Distance to measure user similarities. Experiments on MovieLens dataset show the effectiveness of the proposed approach on both accuracy and diversity.
AB - Recommender systems play increasingly significant roles in solving the information explosion problem. Generally, the user ratings are treated as ground truth of their tastes, and used as index for later predict unknown ratings. However, researchers have found that users are inconsistent in giving their feedbacks, which can be considered as rating noise. Some researchers focus on improving recommendation quality by de-noising user feedbacks. In this paper, we try to improve recommendation quality in a different way. The rating inconsistency is considered as an inherent characteristic of user feedbacks. User rating is described by the probability distribution of user attitude instead of the exact attitude towards the current item. According to it, we propose a recommendation approach based on conventional user-based collaborative filtering using the Manhattan Distance to measure user similarities. Experiments on MovieLens dataset show the effectiveness of the proposed approach on both accuracy and diversity.
KW - Collaborative filtering
KW - Rating inconsistency
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=84910040054&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11538-2_36
DO - 10.1007/978-3-319-11538-2_36
M3 - Conference contribution
AN - SCOPUS:84910040054
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 393
EP - 402
BT - Web-Age Information Management - WAIM 2014 International Workshops
A2 - Chen, Yueguo
A2 - Balke, Wolf-Tilo
A2 - Xu, Jianliang
A2 - Xu, Wei
A2 - Jin, Peiquan
A2 - Lin, Xin
A2 - Tang, Tiffany
A2 - Hwang, Eenjun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 36th German Conference on Pattern Recognition, GCPR 2014
Y2 - 2 September 2014 through 5 September 2014
ER -