Inferring Substitutable and Complementary Products with Knowledge-Aware
  Path Reasoning based on Dynamic Policy Network
  Inferring the substitutable and complementary products for a given product is
an essential and fundamental concern for the recommender system. To achieve
this, existing approaches take advantage of the knowledge graphs to learn more
evidences for inference, whereas they often suffer from invalid reasoning for
lack of elegant decision making strategies. Therefore, we propose a novel
Knowledge-Aware Path Reasoning (KAPR) model which leverages the dynamic policy
network to make explicit reasoning over knowledge graphs, for inferring the
substitutable and complementary relationships. Our contributions can be
highlighted as three aspects. Firstly, we model this inference scenario as a
Markov Decision Process in order to accomplish a knowledge-aware path reasoning
over knowledge graphs. Secondly,we integrate both structured and unstructured
knowledge to provide adequate evidences for making accurate decision-making.
Thirdly, we evaluate our model on a series of real-world datasets, achieving
competitive performance compared with state-of-the-art approaches. Our code is
released on https://gitee.com/yangzijing flower/kapr/tree/master.
