Mining Divergent Opinion Trust Networks through Latent Dirichlet Allocation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Personalized tag recommendation is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. In this paper, we proposed a personalized tag recommendation algorithm incorporating with users' social relations. We model the social annotations made by the collaborative users and the social relations between them with a graph model. We associate each node in this graph with a tag preference vector, which is then refined through a random walk procedure over this graph. The tag preferences of the active user and resource are finally combined to generate the recommended tags. We conduct experiments on the Delicious. Experimental results demonstrate the effectiveness of the proposed algorithm.