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Robust Features for Trustable Aggregation of Online Ratings

Title
Robust Features for Trustable Aggregation of Online Ratings
Author
김상욱
Keywords
Trust; False reputation; Robustness; Robust features; Unfair ratings; Attackers
Issue Date
2016-01
Publisher
ACM IMCOM 2016
Citation
ACM IMCOM 2016: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, Article number a13, Page. 1-7
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. © 2016 ACM.
URI
https://dl.acm.org/citation.cfm?doid=2857546.2857560http://hdl.handle.net/20.500.11754/30635
ISBN
978-145034142-4
DOI
10.1145/2857546.2857560
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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