一种融合协同因子的知识图谱传播推荐模型.

In view of the problems of data sparsity and cold-start in traditional recommender model, the introduction of knowledge graph as side-information can address the above problems and be interpretable. However, knowledge graph is more biased towards propagation of knowledge than user preferences and difficult to capture high-order relations. To solve these problems, collaborative factor module was introduced into propagation-based method in this paper to capture high-order relations and discover latent patterns. In addition, a density gate composed of three co-occurrence matrix density parameters was designed, so that the collaborative factor module could dynamically control the output by the sparsity of the co-occurrence matrix. Contrast experiments were carried out on public film, book and music data sets. The experimental results demonstrate that the model performs well in the click-through-rate scenario, and the indicators are significantly improved on the data sets whose relations of knowledge graph are difficult to explain user preferences. [ABSTRACT FROM AUTHOR]