Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 211-216 |
| Number of pages | 6 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781538613269 |
| DOIs | |
| Publication status | Published - 2017 Dec 28 |
| Event | 10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017 - Kanazawa, Japan Duration: 2017 Nov 22 → 2017 Nov 25 |
Other
| Other | 10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017 |
|---|---|
| Country | Japan |
| City | Kanazawa |
| Period | 17/11/22 → 17/11/25 |
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Keywords
- e-commerce
- recommender system
- social network
- trust-aware
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems and Management
Cite this
Trust-Aware Recommendation for E-Commerce Associated with Social Networks. / Liang, Wei; Zhou, Xiaokang; Huang, Suzhen; Hu, Chunhua; Jin, Qun.
Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 211-216.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
VL - 2017-January
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.
ER -