Robust features for trustable aggregation of online ratings
- Authors
- Oh, Hyun-Kyo; Kim, Sang-Wook
- Issue Date
- Jan-2016
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Attackers; False reputation; Robust features; Robustness; Trust; Unfair ratings
- Citation
- ACM IMCOM 2016: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, pp.1 - 7
- Indexed
- SCOPUS
- Journal Title
- ACM IMCOM 2016: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication
- Start Page
- 1
- End Page
- 7
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/155294
- DOI
- 10.1145/2857546.2857560
- ISSN
- 0000-0000
- Abstract
- When purchasing an online product, customers tend to be influenced strongly by its reputation, the aggregation of customers' ratings on the product. The reputation, however, is not always trustable since it can be easily manipulated by attackers. In this paper, we first address identifying trustable users on a given product in online rating systems, and computing its true reputation by aggregating only their ratings. In order to find these trustable users, we list candidate user features significantly related to the trustworthiness of users and verify the robustness of each user feature through extensive experiments.
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Collections - 서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
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