Modeling eBay-like reputation systems: Analysis, characterization and insurance mechanism design

Abstract

E-commerce systems like eBay are becoming increasingly popular. Having an effective reputation system is critical because it can assist buyers to evaluate the trustworthiness of sellers, and improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for E-commerce operators. By analyzing a dataset from eBay, we discover that eBay suffers a long ramp up time, low long term profit gains and low average per seller transaction gains. We design a novel insurance mechanism consisting of an insurance protocol and a transaction mechanism to improve the above four measures. We formulate an optimization framework to select appropriate parameters for our insurance mechanism. We conduct experiments on an eBay’s dataset and show that our insurance mechanism reduces ramp up time by 91%, improves both the long term profit gains and the average per seller transaction gains by 26.66%. It also guarantees that new sellers drop out with a small probability (close to 0).