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Taxonomies have been widely used in various domains to underpin numerousapplications. Specially, product taxonomies serve an essential role in thee-commerce domain for the recommendation, browsing, and query understanding.However, taxonomies need to constantly capture the newly emerged terms orconcepts in e-commerce platforms to keep up-to-date, which is expensive andlabor-intensive if it relies on manual maintenance and updates. Therefore, wetarget the taxonomy expansion task to attach new concepts to existingtaxonomies automatically. In this paper, we present a self-supervised and userbehavior-oriented product taxonomy expansion framework to append new conceptsinto existing taxonomies. Our framework extracts hyponymy relations thatconform to users' intentions and cognition. Specifically, i) to fully exploituser behavioral information, we extract candidate hyponymy relations that matchuser interests from query-click concepts; ii) to enhance the semanticinformation of new concepts and better detect hyponymy relations, we modelconcepts and relations through both user-generated content and structuralinformation in existing taxonomies and user click logs, by leveragingPre-trained Language Models and Graph Neural Network combined with ContrastiveLearning; iii) to reduce the cost of dataset construction and overcome dataskews, we construct a high-quality and balanced training dataset from existingtaxonomy with no supervision. Extensive experiments on real-world producttaxonomies in Meituan Platform, a leading Chinese vertical e-commerce platformto order take-out with more than 70 million daily active users, demonstrate thesuperiority of our proposed framework over state-of-the-art methods. Notably,our method enlarges the size of real-world product taxonomies from 39,263 to94,698 relations with 88% precision.

Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision