An Interactive System for Unfair Rating Detection Models in a Customized Perspective.

Strangers build trustworthiness through reputation systems in various online platforms. A reputation system collects history ratings from users about an object/entity and aggregates them as a reputation score, for the reference of future potential users. The reputation score can accurately reflect the real quality of this object if all the ratings are fairly provided, otherwise it may mislead other users if the ratings are unfairly provided. In order to mitigate the impacts of unfair ratings, many unfair rating detection models have been studied in the recent years, through identifying and filtering out the unfair ratings. In this work, we aim to investigate the existing unfair rating detection models considering realistic application settings in an interactive approach where the process of the unfair ratings detection is conducted by involving the interactions between the application system designer and these models. Based on this idea, we design a customized interactive system (CIS) which can satisfy the customized demands of application system designers through five customized functions, i.e., customized scenes, customized attack, customized model, customized metrics, and customized result presentations. After a series of interactions, application system designers can obtain the detection model that best fulfills their demands and the corresponding optimal parameters. To present the applicability of the proposed CIS system, we analyze several typical reputation models in our experiments and the experimental results indicate that our work can effectively bridge the existing unfair rating detection models with realistic applications.