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Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for top-N recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users.