  Incorporating knowledge graph (KG) into recommender system is promising in
improving the recommendation accuracy and explainability. However, existing
methods largely assume that a KG is complete and simply transfer the
"knowledge" in KG at the shallow level of entity raw data or embeddings. This
may lead to suboptimal performance, since a practical KG can hardly be
complete, and it is common that a KG has missing facts, relations, and
entities. Thus, we argue that it is crucial to consider the incomplete nature
of KG when incorporating it into recommender system.
  In this paper, we jointly learn the model of recommendation and knowledge
graph completion. Distinct from previous KG-based recommendation methods, we
transfer the relation information in KG, so as to understand the reasons that a
user likes an item. As an example, if a user has watched several movies
directed by (relation) the same person (entity), we can infer that the director
relation plays a critical role when the user makes the decision, thus help to
understand the user's preference at a finer granularity.
  Technically, we contribute a new translation-based recommendation model,
which specially accounts for various preferences in translating a user to an
item, and then jointly train it with a KG completion model by combining several
transfer schemes. Extensive experiments on two benchmark datasets show that our
method outperforms state-of-the-art KG-based recommendation methods. Further
analysis verifies the positive effect of joint training on both tasks of
recommendation and KG completion, and the advantage of our model in
understanding user preference. We publish our project at
https://github.com/TaoMiner/joint-kg-recommender.
