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Collaborative tagging has been very popular with the development of the Web 2.0, which helps users manage, share and utilize resources effectively. For various kinds of resources, the way to recommend appropriate resources to right users is the key problem in tagging system. This paper proposes a user taste diffusion model based on the tripartite hypergraph to deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation. Through the defined tri-relation model and diffusion probability matrix, the user’s taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user’s direct neighbors but also cover the ones which are collected by his/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods.

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