The HMM-based model for evaluating recommender´s reputation

There is limited research on evaluating an agent´s reputation as a recommender. A key challenge is that a recommender´s reputation is affected by both the recommender´s trustworthiness and the recommender´s expertise, including the recommender´s trust knowledge of others and the reliability of the recommender´s trust evaluation models. We give an ordered depth-first search with threshold (ODFST) algorithm to find the optimal referral chain. We then develop a hidden Markov model (HMM) based approach to measure an agent´s reputation as a recommender. This approach models chained recommendation events as an HMM. The features of the trust model are: (1) no explicit requirement of chained recommendation reputations; (2) flexible recommendation network with presence of loops; and (3) integration of learning speed into trust evaluation reliability. The experimental results showed the convergence and reliability of the proposed trust model