Learning user credibility for product ranking

As the explosion of user-generated data (UGC) in electronic commerce, this kind of data is scanned for trust or credibility calculation, which plays an important role in business selection. The commonly used UGC is user reviews and ratings. A new consumer without any experience with some product will read these UGCs to get an overview. However, the open and dynamic e-commerce platforms may rise the generation of unfair or deceitful reviews and ratings. Then, detecting trustful reviewers or generating authentic ratings for customers is urgent and useful. In this paper, we present a twin-bipartite graph model to catch the review and ranking relationship among users, products and shops. We design a feedback mechanism to get the consistent ranking among different level of objects, which are users and items. In the algorithm, we adjust customer credibility values by the feedback considering the rating consistency; we adjust ratings by combining customer credibility together with originally assigned ratings. 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. We detect the inconsistency between semantic ratings (the review comments) and numerical ratings (scores). To deal with it, we train a classifier on the training data that are constructed automatically. The trained classifier is used to predict the semantic scores from review comments. Finally, we calculate the scores of products by considering both the customer credibility and the predicted scores. We conduct experiments using a large amount of real-world data. The experimental results show that our proposed approach provides better products ranking than the baseline systems.