TY - GEN
T1 - Trust-Aware Recommendation for E-Commerce Associated with Social Networks
AU - Liang, Wei
AU - Zhou, Xiaokang
AU - Huang, Suzhen
AU - Hu, Chunhua
AU - Jin, Qun
N1 - Funding Information:
The work has been partially supported by 2017 Waseda University Grants for Special Research Projects No. 2017B-302, the National Science Foundation of China under Grant Nos. 61273232, 61472136, the Program for New Century Excellent Talents in University under NCET-13-0785, and the Hunan Provincial Education Department Foundation for Excellent Youth Scholars under Grant No. 17B146.
PY - 2017/12/28
Y1 - 2017/12/28
N2 - In recent years, recommender systems are widely applied in e-commerce system to help users locating their interested information. However, the 'all good reputation' problem brings down the accuracy of recommender systems. In addition, users' social network can benefit the recommendation especially when dealing with cold-start scenarios. In this paper, a novel trust-aware recommendation approach for e-commerce is proposed, which unearths the hint from ordinary rating and trust network by users' instant interactions in e-commerce system. More precisely, a rating revamping algorithm is designed to extract semantic ratings from feedback comments, and further construct fine grained rating score for the next process. Then, the recommendation scheme is studied through analyzing the users' trust network and their own behavior in e-commerce system. Finally, evaluations conducted based on a real dataset 'Douban' to demonstrate the effectiveness of the proposed method.
AB - In recent years, recommender systems are widely applied in e-commerce system to help users locating their interested information. However, the 'all good reputation' problem brings down the accuracy of recommender systems. In addition, users' social network can benefit the recommendation especially when dealing with cold-start scenarios. In this paper, a novel trust-aware recommendation approach for e-commerce is proposed, which unearths the hint from ordinary rating and trust network by users' instant interactions in e-commerce system. More precisely, a rating revamping algorithm is designed to extract semantic ratings from feedback comments, and further construct fine grained rating score for the next process. Then, the recommendation scheme is studied through analyzing the users' trust network and their own behavior in e-commerce system. Finally, evaluations conducted based on a real dataset 'Douban' to demonstrate the effectiveness of the proposed method.
KW - e-commerce
KW - recommender system
KW - social network
KW - trust-aware
UR - http://www.scopus.com/inward/record.url?scp=85046966002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046966002&partnerID=8YFLogxK
U2 - 10.1109/SOCA.2017.36
DO - 10.1109/SOCA.2017.36
M3 - Conference contribution
AN - SCOPUS:85046966002
T3 - Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017
SP - 211
EP - 216
BT - Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017
Y2 - 22 November 2017 through 25 November 2017
ER -