Filtering unfair users a Hidden Markov Model approach

We propose a method based on Hidden Markov Models (HMM) for filtering out users who submit unfair ratings to an online reputation system. We assume that users are fair, positively biased, and negatively biased, and each user submits a number of ratings. A different HMM (A, B, n) is used to describe each of the three types of user, with the only difference in these HMMs being matrix B. For each user, we train an HMM based on the user's ratings, and subsequently the B matrices of the trained HMMs are clustered together into two groups: fair and unfair (both positively and negatively biased), which permits us to identify the group of fair users. The A matrices of the filtered fair users are then aggregated in order to estimate the A matrix of the quality of the service under study. Also, using the HMMs of the fair users, we obtain the most probable current state of the quality of the service.