A Fast-Effective Algorithm on A Concise Representation of Top Rated Utility Mining Datasets

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose Trust-reputation, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of Trust-reputation through extensive experiments in comparisons to state-of-the-art approaches.