EER: Enterprise Expert Ranking using Employee Reputation

TL;DR

This paper introduces a Bayesian reputation model for ranking enterprise employees, addressing issues like collusion and reputation inflation, and demonstrating improved accuracy over existing methods.

Contribution

It proposes a novel Bayesian approach using beta distribution for employee ranking, enhancing robustness and accuracy in dynamic enterprise environments.

Findings

7% improvement in precision over previous methods

Effective differentiation of interaction categories in dynamic contexts

Independence from rating pattern and data density

Abstract

The emergence of online enterprises spread across continents have given rise to the need for expert identification in this domain. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee that is not documented or self-disclosed has been addressed in this article. The existing reputation based approaches towards expertise ranking in enterprises utilize PageRank, normal distribution, and hidden Markov model for expertise ranking. These models suffer issue of negative referral, collusion, reputation inflation, and dynamism. The authors have however proposed a Bayesian approach utilizing beta probability distribution based reputation model for employee ranking in enterprises. The experimental results reveal improved performance compared to previous techniques in terms of Precision and Mean Average Error (MAE) with almost 7% improvement in precision on average for the three data sets. The proposed technique is able to differentiate categories of interactions in a dynamic context. The results reveal that the technique is independent of the rating pattern and density of data.