Towards an objective ranking in online reputation systems: the effect of the rating projection

TL;DR

This paper introduces a rating projection method to preprocess user ratings in online reputation systems, improving the accuracy of item ranking algorithms by accounting for nonlinear user preferences.

Contribution

The paper proposes a novel rating projection technique as a data preprocessing step to enhance ranking accuracy in online reputation systems.

Findings

Projected ratings improve ranking algorithm performance

Simulation results on artificial and real networks validate effectiveness

Rating projection captures nonlinear user preferences

Abstract

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.