UIP: estimating true rating scores of services through online user communities

As many online systems rely on user ratings for making decisions such as recommendations, the quality of such rating scores are increasingly important. On the other hand, users interact with each other via online communities. How such interactions affect the trueness of their ratings? Can we obtain the true rating scores that exclude the influences among users? This paper presents a conceptual framework that characterizes the influences on quality of services among users, and an algorithm that estimates the true rating scores by minimizing the influence among users. In other words, the influence on users' ratings due to their interactions is minimized so as to obtain the more accurate rating scores. The proposed approach has been validated by experimenting on real data sets. The results of the experiments have demonstrated that our approach is capable of estimating true ratings.