标题： 电子商务搜索广告的多目标对齐出价生成模型 显示英文标题

标题： Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising

摘要： 检索系统主要解决的是将用户查询与最相关的广告相匹配的挑战，在电子商务搜索广告中发挥着至关重要的作用。 用户需求和表达方式的多样性常常导致大量的长尾查询无法与商家的竞价词或产品标题匹配，从而导致某些广告未能被召回，最终损害了用户体验和搜索效率。 现有的查询改写研究主要集中于各种方法，如查询日志挖掘、查询-竞价词向量匹配或基于生成的改写。 然而，这些方法往往无法同时优化用户原始查询的相关性和真实性，并重新改写以最大化召回广告的收入潜力。 在本文中，我们提出了一个多目标对齐竞价词生成模型（MoBGM），它由判别器、生成器和偏好对齐模块组成，以解决这些挑战。 为了同时提高查询的相关性和真实性以及改写的性能并最大化平台收入，我们设计了一个判别器来优化这些关键目标。 利用判别器的反馈信号，我们训练了一个多目标对齐的竞价词生成器，旨在最大化这三个目标的综合效果。 广泛的离线和在线实验表明，我们提出的算法显著优于现有技术。 部署后，该算法为平台创造了巨大的商业价值，进一步验证了其可行性和鲁棒性。 显示英文摘要

摘要： Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements, playing a crucial role in e-commerce search advertising. The diversity of user needs and expressions often produces massive long-tail queries that cannot be matched with merchant bidwords or product titles, which results in some advertisements not being recalled, ultimately harming user experience and search efficiency. Existing query rewriting research focuses on various methods such as query log mining, query-bidword vector matching, or generation-based rewriting. However, these methods often fail to simultaneously optimize the relevance and authenticity of the user's original query and rewrite and maximize the revenue potential of recalled ads. In this paper, we propose a Multi-objective aligned Bidword Generation Model (MoBGM), which is composed of a discriminator, generator, and preference alignment module, to address these challenges. To simultaneously improve the relevance and authenticity of the query and rewrite and maximize the platform revenue, we design a discriminator to optimize these key objectives. Using the feedback signal of the discriminator, we train a multi-objective aligned bidword generator that aims to maximize the combined effect of the three objectives. Extensive offline and online experiments show that our proposed algorithm significantly outperforms the state of the art. After deployment, the algorithm has created huge commercial value for the platform, further verifying its feasibility and robustness.