Recommender Systems (RS) aim to suggest users with items that they might like based on users' opinion on items. In practice, information about the users' opinion on items is usually sparse compared to the vast information about users and items. Therefore it is hard to analyze and justify users' favorites, particularly those of cold start users. In this paper, we propose a trust model based on the user trust network, which is composed of the trust relationships among users. We also introduce the widely used conceptual model Topic Map, with which we try to classify items into topics for Recommender analysis. We novelly combine trust relations among users with Topic Maps to resolve the sparsity problem and cold start problem. The evaluation shows our model and method can achieve a good recommendation effect.