  How to predict precise user preference and how to make efficient retrieval
from a big corpus are two major challenges of large-scale industrial
recommender systems. In tree-based methods, a tree structure T is adopted as
index and each item in corpus is attached to a leaf node on T . Then the
recommendation problem is converted into a hierarchical retrieval problem
solved by a beam search process efficiently. In this paper, we argue that the
tree index used to support efficient retrieval in tree-based methods also has
rich hierarchical information about the corpus. Furthermore, we propose a novel
context-aware tree-based deep model (ConTDM) for recommender systems. In
ConTDM, a context-aware user preference prediction model M is designed to
utilize both horizontal and vertical contexts on T . Horizontally, a graph
convolutional layer is used to enrich the representation of both users and
nodes on T with their neighbors. Vertically, a parent fusion layer is designed
in M to transmit the user preference representation in higher levels of T to
the current level, grasping the essence that tree-based methods are generating
the candidate set from coarse to detail during the beam search retrieval.
Besides, we argue that the proposed user preference model in ConTDM can be
conveniently extended to other tree-based methods for recommender systems. Both
experiments on large scale real-world datasets and online A/B test in large
scale industrial applications show the significant improvements brought by
ConTDM.
