TY - GEN
T1 - Landscape recommendation system using public preference mining and social influence analysis
AU - Tsai, Wen Hao
AU - Lin, Yan Ting
AU - Lee, Kuan Rung
AU - Kuo, Yau-Hwang
AU - Lu, Bing Huei
PY - 2015/1/1
Y1 - 2015/1/1
N2 - A novel landscape recommendation system which employs public preference and social influence to classify user preference orientation is proposed in this paper. Unlike traditional content-based or collaborative filtering recommendation approaches, we collected large scale information from heterogeneous data sources to construct the public preference model for user's feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. Then, the social influence of preference is calculated by social influence and interest similarity between users. The purpose of this paper is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. The proposed method deals with the cold-start issue in recommendation system. There two main advantages of the proposed method are social relationship can be easily obtained from online social network and any type of recommendation system can be applied in the proposed method. In our experiment, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable.
AB - A novel landscape recommendation system which employs public preference and social influence to classify user preference orientation is proposed in this paper. Unlike traditional content-based or collaborative filtering recommendation approaches, we collected large scale information from heterogeneous data sources to construct the public preference model for user's feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. Then, the social influence of preference is calculated by social influence and interest similarity between users. The purpose of this paper is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. The proposed method deals with the cold-start issue in recommendation system. There two main advantages of the proposed method are social relationship can be easily obtained from online social network and any type of recommendation system can be applied in the proposed method. In our experiment, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable.
UR - http://www.scopus.com/inward/record.url?scp=84926433969&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926433969&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-484-8-583
DO - 10.3233/978-1-61499-484-8-583
M3 - Conference contribution
AN - SCOPUS:84926433969
T3 - Frontiers in Artificial Intelligence and Applications
SP - 583
EP - 592
BT - Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
A2 - Chu, William Cheng-Chung
A2 - Yang, Stephen Jenn-Hwa
A2 - Chao, Han-Chieh
PB - IOS Press
T2 - International Computer Symposium, ICS 2014
Y2 - 12 December 2014 through 14 December 2014
ER -