Trust-Aware Recommendation for E-Commerce Associated with Social Networks

Wei Liang; Xiaokang Zhou; Suzhen Huang; Chunhua Hu; Qun Jin

doi:10.1109/SOCA.2017.36

Trust-Aware Recommendation for E-Commerce Associated with Social Networks

Wei Liang, Xiaokang Zhou, Suzhen Huang, Chunhua Hu, Qun Jin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/SOCA.2017.36
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

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

Access to Document

10.1109/SOCA.2017.36

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Cite this

Liang, W., Zhou, X., Huang, S., Hu, C., & Jin, Q. (2017). Trust-Aware Recommendation for E-Commerce Associated with Social Networks. In Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017 (Vol. 2017-January, pp. 211-216). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SOCA.2017.36

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

Liang, W, Zhou, X, Huang, S, Hu, C & Jin, Q 2017, Trust-Aware Recommendation for E-Commerce Associated with Social Networks. in Proceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 211-216, 10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017, Kanazawa, Japan, 17/11/22. https://doi.org/10.1109/SOCA.2017.36

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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.",

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