An extended neuro-fuzzy approach for efficiently predicting review ratings in E-markets

Internet has opened new interesting scenarios in the fields of commerce and marketing. In particular, the idea of e-commerce has enabled customers to perform their transactions in a faster and cheaper way than conventional markets, and it has allowed companies to increase their sales volume thanks to a world-wide visibility. However, one of the problems that can strongly affect the performance of any e-commerce portal is related to the quality and validity of ratings provided by customers in their past transactions. Indeed, these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service and they can affect the future selling performance and market share of a company. As a consequence, an efficient analysis of customer feedback could allow e-commerce portals to improve their selling capabilities and revenue. This paper introduces an innovative computational intelligence framework for efficiently learning review ratings in e-commerce by addressing different issues involved in this significant task: the dimension and imprecision of ratings data. In particular, we integrate the techniques of Singular Value Decomposition (SVD), Fuzzy C-Means (FCM) and ANFIS and, as shown in experimental results, this synergetic approach yields better learning performance than other rating predictors based on a conventional artificial neural network and FCM algorithm. © 2014 IEEE.