Rating prediction via exploring service reputation

Abstract

With the explosion of e-commerce, it presents a great opportunity for people to share their consumption experience in review websites. However, at the same time we face the information overloading problem. How to mine valuable information from these reviews and make an accurate recommendation is crucial for us. Traditional recommender systems (RS) consider many factors, such as product category, geographic location, user's purchase records, and the other social network factors. In this paper, we firstly propose a social user's reviews sentiment measurement approach and calculate each user's sentiment score on items/services. Secondly, we consider service reputation, which reflects the customers' comprehensive evaluation. At last, we fuse service reputation factor into our recommender system to make an accurate rating prediction, which is based on probabilistic matrix factorization. We conduct a series of experiments on Yelp dataset, and experimental results show the proposed approach outperforms the existing RS approaches.