  Large-scale industrial recommender systems are usually confronted with
computational problems due to the enormous corpus size. To retrieve and
recommend the most relevant items to users under response time limits,
resorting to an efficient index structure is an effective and practical
solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning}
greatly improves recommendation accuracy using tree index. By indexing items in
a tree hierarchy and training a user-node preference prediction model
satisfying a max-heap like property in the tree, TDM provides logarithmic
computational complexity w.r.t. the corpus size, enabling the use of arbitrary
advanced models in candidate retrieval and recommendation.
  In tree-based recommendation methods, the quality of both the tree index and
the user-node preference prediction model determines the recommendation
accuracy for the most part. We argue that the learning of tree index and
preference model has interdependence. Our purpose, in this paper, is to develop
a method to jointly learn the index structure and user preference prediction
model. In our proposed joint optimization framework, the learning of index and
user preference prediction model are carried out under a unified performance
measure. Besides, we come up with a novel hierarchical user preference
representation utilizing the tree index hierarchy. Experimental evaluations
with two large-scale real-world datasets show that the proposed method improves
recommendation accuracy significantly. Online A/B test results at a display
advertising platform also demonstrate the effectiveness of the proposed method
in production environments.
