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 |
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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 |
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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