Knowledge Graph Convolutional Networks for Recommender Systems

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their…

• MR
  Microsoft Research
• NN
  National Natural Science Foundation of China

  Awards: 61272291, 2015CB352403
• MR
  Microsoft Research Asia
• NK
  National Key Research and Development Program of China

  Award: 2015CB352403