Abstract This study aims to predict customers’ behavior in classifying their reviews as high rated or low using associated attributes and topics found the review. Knowing customer reviewing action better can lead a successful strategy implementation of relevant parties related this such policy manage by keeping satisfaction high. We applied big data approach on dataset 55,377 from Airbnb listin...

Peer-to-peer systems are an attractive means of sharing data and services. However, the problem of how to efficiently decide which peers are to be trusted still remains unsolved. In this work we propose a decentralized trust management system based on reputation, for unstructured, self-organizing peer-to-peer networks. Our protocol takes advantage of the unstructured nature of the network to re...

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Our interest in this work is to automatically predict whether peer ratings have high or low agreement in terms of disparity with instructor ratings, using solely features extracted from quantitative peer ratings and text-based peer comments. Experimental results suggest that our model can indeed outperform a majority baseline in predicting low versus high rating disparity. Furthermore, the reli...

We empirically investigate how hosts on Airbnb, a popular peer-to-peer website for fee-based sharing of under-utilized space, adjust their prices once their offering gets a visible star rating for the first time. We use data for over 14,000 offerings from Airbnb which we collected for New York City. Our findings indicate that hosts whose offerings achieve star rating visibility significantly in...