Credibility-based product ranking for C2C transactions

Abstract

A fundamental issue for C2C transactions is how to rank the products based on the reviews written by the previous customers. In this paper, we present an approach to improve products ranking by tackling the noisy ratings that exist in the practical systems. The first problem is the credibility of the customers. We design an iterative algorithm to measure the customer credibility. In the algorithm, we use a feedback strategy to increase or decrease the customer credibility. We increase the credibility for a customer if the customer gives a high (low) score to a good (bad) product and decrease the value if the customer gives a low (high) score to a good (bad) product. The second problem is the inconsistency between the review comments and scores. To deal with it, we train a classifier on a training data that is constructed automatically. The trained classifier is used to predict the scores of the comments. Finally, we calculate the scores of products by considering the customer credibility and the predicted scores. The experimental results show that our proposed approach provides better products ranking than the baseline systems.