Abstract
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
Abstract (translated)
随着短视频行业的快速发展，传统电子商务遇到了一个新的范式，即视频驱动的电子商务，它利用吸引人的视频进行产品展示，并为用户提供视频和商品服务。受益于动态和可视化的商品介绍，视频驱动的电子商务在刺激消费者信心和促进销售方面显示出巨大的潜力。在本文中，我们专注于视频检索任务，面临以下挑战：（1）如何处理用户、商品和视频之间的异质性？ （2）如何从视频中挖掘物品和视频之间的互补性，以便更好地理解用户？本文中，我们首先利用双图建模用户-视频和用户-商品之间的交互，然后创新地将用户偏好理解降低到图匹配问题。为解决这个问题，我们进一步提出了新颖的 bi- 级图匹配网络（GMN），主要由节点和偏好级图匹配组成。给定一个用户，节点级图匹配旨在匹配视频和商品，而偏好级图匹配旨在匹配从视频和商品中提取的多个用户偏好。然后，所提出的 GMN可以通过双级聚合从双图中匹配的节点或偏好进行生成和提高用户嵌入。全面的实验证明，与最先进的解决方案相比（例如 AUC+1.9% 和 CTR+7.15%），所提出的 GMN 的优越性非常明显。我们还在一个著名的视频驱动电子商务平台上开发了它，每天为数亿人提供服务。
URL
https://arxiv.org/abs/2408.00346
