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The concept of Web 2.0 or "semantic web" has been getting more and more popular during the last half decade. The potential of very subtle yet important emergent semantics hidden in such environments calls for equally elegant and powerful methods to "mine" them. However, much of the previous work on model based recommender systems for folksonomies considered user to resource and resource to tag similarity separately, ignoring the dependency of users' interest to both the tags and the corresponding resources. In this paper, we propose a probabilistic personalized recommendation model, Latent Interest Model, that accounts for users, tags and resources jointly. The proposed method's performance is evaluated on real data sets obtained from a popular online bookmarking site using different performance measures for tag and resource recommendation tasks. Our experimental results show that our model captures personal preferences for tag usage and resource selection. Performance evaluation of Latent Interest Model indicates that the proposed personalized method yields significant improvement of recommendation accuracy.