Reputation-based Ranking Systems and their Resistance to Bribery

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Saude, J (Author) Other; Ramos, G (Author) Other; Caleiro, C (Author) Other; Kar, S (Author) Other

Conference proceedings International

Title: 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)

Pages: 1063-1068

17th IEEE International Conference on Data Mining (ICDMW)

New Orleans, LA, NOV 18-21, 2017

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00N-RK6

DOI: 10.1109/icdm.2017.139

Abstract (EN): We study bribery resistance properties in two classes of reputation-based ranking systems, where the rankings are computed by weighting the rates given by users with their reputations. In the first class, the rankings are the result of the aggregation of all the ratings, and all users are provided with the same ranking for each item. In the second class, there is a first step that clusters users by their rating pattern similarities, and then the rankings are computed cluster-wise. Hence, for each item, there is a different ranking for distinct clusters. We study the setting where the seller of each item can bribe users to rate the item, if they did not rate it before, or to increase their previous rating on the item. We model bribing strategies under these ranking scenarios and explore under which conditions it is profitable to bribe a user, presenting, in several cases, the optimal bribing strategies. By computing dedicated rankings to each cluster, we show that bribing, in general, is not as profitable as in the simpler without clustering. Finally, we illustrate our results with experiments using real data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6