Deflated reputation using multiplicative long short-term memory neural networks

Current reputation systems are facing the inflation problem, which renders reputation systems to lose information and sometimes even cause misunderstandings. To address this problem, we propose a data-driven approach that combines natural language processing techniques with the conditional logit model for reputation deflation. We consider multiplicative long short-term memory neural networks (mLSTM) to predict sentiment scores from the feedback content. The mLSTM was pre-trained on 82.83 million unique reviews. We conduct experiments on one of the largest online labor marketplaces, Freelancer.com. We focus on comparing ratings and predicted sentiment scores in the online labor market. The results show that our proposed model can estimate deflated reputation information effectively. In addition, the estimated sentiment score is a quality disclosure signal, and has a better effect on the market outcome than the inflated reputation rating. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a data-driven approach that combines natural language processing techniques with the conditional logit model to address the inflation problem in reputation systems. By pre-training a multiplicative long short-term memory neural network, the proposed model effectively estimates deflated reputation information and provides better market outcomes.