基于潜在标签挖掘和细粒度偏好的个性化标签推荐.

To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference . Firstly, BPR-PITF-P leveraged the context information of both users and got to mine potential and useful tags, and got three kinds of tags, positive tags, potential tags, and negative tags. Based on the above, it translated the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helped alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P modeled the preference relationship based on the optimization criteria of Bayesian personalized ranking, and developed a personalized tag recommendation model followed by optimization algorithm. The comparison results show that this proposed method could improve tag recommendation performance in the premise of guarantee convergence speed. [ABSTRACT FROM AUTHOR]