Abstract Since most of the existing personalized recommendation algorithms pursue a higher accuracy, their performance is affected by the problem of user interest over-specialization. An algorithm is proposed to fully mine and use the implicit user interest information for recommendation. The probabilistic topic model is adopted to extract user interest distribution, and the weighted tripartite graph of user-interest-item is generated. Then the user item resource value is allocated by material diffusion algorithm in user-interest and interest-item bipartite graphs respectively, and the Top-K recommendation list is generated according to the rank of item resource values. Experimental results on Movielens datasets show the proposed algorithm relieves the problem of user interest over-specialization. Meanwhile the recommendation accuracy is improved .
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