Towards dynamic interaction-based model

TL;DR

This paper introduces DIB-RM, a dynamic reputation model based on forgetting, cumulative, and activity factors, evaluated using StackOverflow data to improve trustworthiness in user ranking systems.

Contribution

The paper proposes a novel dynamic reputation model (DIB-RM) that incorporates forgetting, cumulative, and activity factors, and evaluates its effectiveness with real-world data.

Findings

Historical reputation yields better similarity metrics.

Model effectiveness varies with different parameter settings.

Preliminary results show promise for dynamic reputation modeling.

Abstract

In this paper, we investigate how dynamic properties of reputation can influence the quality of users ranking. Reputation systems should be based on rules that can guarantee a high level of trust and help identifying unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that is based on three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow, which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow model so to check our hypothesis. We use two values to calculate our metric: DIB-RM reputation and $historical$ reputation. We found that $historical$ reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.