Towards an objective ranking in online reputation systems: the effect of the rating projection
This work addresses a fundamental issue in e-commerce reputation systems for providers and users, but it is incremental as it focuses on data pretreatment rather than new algorithms.
The paper tackles the problem of generating objective rankings in online reputation systems by addressing the nonlinear nature of user preferences through rating projection, showing that this data pretreatment method improves ranking algorithm performance in simulations on artificial and real networks.
Online reputation systems are commonly used by e-commerce providers nowadays. In order to generate an objective ranking of online items' quality according to users' ratings, many sophisticated algorithms have been proposed in the literature. In this paper, instead of proposing new algorithms we focus on a more fundamental problem: the rating projection. The basic idea is that even though the rating values given by users are linearly separated, the real preference of users to items between different values gave is nonlinear. We thus design an approach to project the original ratings of users to more representative values. This approach can be regarded as a data pretreatment method. Simulation in both artificial and real networks shows that the performance of the ranking algorithms can be improved when the projected ratings are used.