  With the rapid development of the short video industry, traditional
e-commerce has encountered a new paradigm, video-driven e-commerce, which
leverages attractive videos for product showcases and provides both video and
item services for users. Benefitting from the dynamic and visualized
introduction of items,video-driven e-commerce has shown huge potential in
stimulating consumer confidence and promoting sales. In this paper, we focus on
the video retrieval task, facing the following challenges: (1) Howto handle the
heterogeneities among users, items, and videos? (2)How to mine the
complementarity between items and videos for better user understanding? In this
paper, we first leverage the dual graph to model the co-existing of user-video
and user-item interactions in video-driven e-commerce and innovatively reduce
user preference understanding to a graph matching problem. To solve it, we
further propose a novel bi-level Graph Matching Network(GMN), which mainly
consists of node- and preference-level graph matching. Given a user, node-level
graph matching aims to match videos and items, while preference-level graph
matching aims to match multiple user preferences extracted from both videos and
items. Then the proposed GMN can generate and improve user embedding by
aggregating matched nodes or preferences from the dual graph in a bi-level
manner. Comprehensive experiments show the superiority of the proposed GMN with
significant improvements over state-of-the-art approaches (e.g., AUC+1.9% and
CTR+7.15%). We have developed it on a well-known video-driven e-commerce
platform, serving hundreds of millions of users every day
