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

翻译：随着短视频产业的快速发展，传统电商迎来了一种新范式——视频驱动电商，其利用富有吸引力的视频进行商品展示，并为用户同时提供视频与商品服务。得益于商品动态化、可视化的介绍方式，视频驱动电商在激发消费者信心和促进销售方面展现出巨大潜力。本文聚焦于视频检索任务，面临以下挑战：(1) 如何处理用户、商品与视频之间的异构性？(2) 如何挖掘商品与视频之间的互补性以更好地理解用户？本文首先利用对偶图对视频驱动电商中用户-视频与用户-商品交互的共存关系进行建模，并将用户偏好理解创新性地规约为图匹配问题。为解决该问题，我们进一步提出一种新颖的双层图匹配网络(GMN)，其主要包括节点级与偏好级图匹配。给定用户，节点级图匹配旨在匹配视频与商品，而偏好级图匹配则旨在匹配从视频和商品中提取的多个用户偏好。随后，所提出的GMN能够通过以双层方式聚合来自对偶图的匹配节点或偏好，生成并优化用户嵌入。综合实验表明，所提出的GMN具有优越性，相较于现有最优方法有显著提升（例如AUC+1.9%，CTR+7.15%）。我们已在知名视频驱动电商平台上部署该系统，每日为数亿用户提供服务。