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.

Neural Information Processing Systems http://nips.cc/

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.

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.

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. Tree-based Deep Model (TDM) for recommendation \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 trained user preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and user 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 Taobao display advertising also demonstrate the effectiveness of the proposed method in production environments.