A topic-biased user reputation model in rating systems

In rating systems like Epinions and Amazon's product review systems, users rate items on different topics to yield item scores. Traditionally, item scores are estimated by averaging all the ratings with equal weights. To improve the accuracy of estimated item scores, user reputation [a.k.a., user reputation (UR)] is incorporated. The existing algorithms on UR, however, have underplayed the role of topics in rating systems. In this paper, we first reveal that UR is topic-biased from our empirical investigation. However, existing algorithms cannot capture this characteristic in rating systems. To address this issue, we propose a topic-biased model (TBM) to estimate UR in terms of different topics as well as item scores. With TBM, we develop six topic-biased algorithms, which are subsequently evaluated with experiments using both real-world and synthetic data sets. Results of the experiments demonstrate that the topic-biased algorithms effectively estimate UR across different topics and produce more robust item scores than previous reputation-based algorithms, leading to potentially more robust rating systems.