  Taxonomies have been widely used in various domains to underpin numerous
applications. Specially, product taxonomies serve an essential role in the
e-commerce domain for the recommendation, browsing, and query understanding.
However, taxonomies need to constantly capture the newly emerged terms or
concepts in e-commerce platforms to keep up-to-date, which is expensive and
labor-intensive if it relies on manual maintenance and updates. Therefore, we
target the taxonomy expansion task to attach new concepts to existing
taxonomies automatically. In this paper, we present a self-supervised and user
behavior-oriented product taxonomy expansion framework to append new concepts
into existing taxonomies. Our framework extracts hyponymy relations that
conform to users' intentions and cognition. Specifically, i) to fully exploit
user behavioral information, we extract candidate hyponymy relations that match
user interests from query-click concepts; ii) to enhance the semantic
information of new concepts and better detect hyponymy relations, we model
concepts and relations through both user-generated content and structural
information in existing taxonomies and user click logs, by leveraging
Pre-trained Language Models and Graph Neural Network combined with Contrastive
Learning; iii) to reduce the cost of dataset construction and overcome data
skews, we construct a high-quality and balanced training dataset from existing
taxonomy with no supervision. Extensive experiments on real-world product
taxonomies in Meituan Platform, a leading Chinese vertical e-commerce platform
to order take-out with more than 70 million daily active users, demonstrate the
superiority of our proposed framework over state-of-the-art methods. Notably,
our method enlarges the size of real-world product taxonomies from 39,263 to
94,698 relations with 88% precision.
