A maximum entropy approach to natural language processing
Computational Linguistics
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Finding the bias and prestige of nodes in networks based on trust scores
Proceedings of the 20th international conference on World wide web
IEEE Transactions on Knowledge and Data Engineering
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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.