  The large-scale recommender system mainly consists of two stages: matching
and ranking. The matching stage (also known as the retrieval step) identifies a
small fraction of relevant items from billion-scale item corpus in low latency
and computational cost. Item-to-item collaborative filter (item-based CF) and
embedding-based retrieval (EBR) have been long used in the industrial matching
stage owing to its efficiency. However, item-based CF is hard to meet
personalization, while EBR has difficulty in satisfying diversity. In this
paper, we propose a novel matching architecture, Path-based Deep Network (named
PDN), which can incorporate both personalization and diversity to enhance
matching performance. Specifically, PDN is comprised of two modules: Trigger
Net and Similarity Net. PDN utilizes Trigger Net to capture the user's interest
in each of his/her interacted item, and Similarity Net to evaluate the
similarity between each interacted item and the target item based on these
items' profile and CF information. The final relevance between the user and the
target item is calculated by explicitly considering user's diverse interests,
\ie aggregating the relevance weights of the related two-hop paths (one hop of
a path corresponds to user-item interaction and the other to item-item
relevance). Furthermore, we describe the architecture design of a matching
system with the proposed PDN in a leading real-world E-Commerce service (Mobile
Taobao App). Based on offline evaluations and online A/B test, we show that PDN
outperforms the existing solutions for the same task. The online results also
demonstrate that PDN can retrieve more personalized and more diverse relevant
items to significantly improve user engagement. Currently, PDN system has been
successfully deployed at Mobile Taobao App and handling major online traffic.
