用大模型生成个性化邮件标题，提升用户点击率。
Using item recommendations and LLMs in marketing email titles
- 用大语言模型根据推荐内容生成主题化邮件标题。
- 线上实验覆盖数百万用户，显著提升点击与互动。
- 适合大规模个性化营销场景，尤其适合电商推送。
电商平台通过邮件、推送通知等渠道触达用户并刺激购买。个性化邮件是营销的重要触点，尤其针对长时间未访问的用户，向其推送符合兴趣的商品推荐。然而，这类邮件的主要入口——标题——常采用固定模板，难以激发用户兴趣。本文探索利用大语言模型（LLMs）生成反映邮件内容主题的个性化标题。通过离线模拟和在线实验，覆盖数百万用户，验证了该方法在提升用户与邮件互动方面的有效性。研究还总结了在生产环境中安全自动化生成邮件标题的关键发现与实践经验。
原文摘要 · Abstract (English)
E-commerce marketplaces make use of a number of marketing channels like emails, push notifications, etc. to reach their users and stimulate purchases. Personalized emails especially are a popular touch point for marketers to inform users of latest items in stock, especially for those who stopped visiting the marketplace. Such emails contain personalized recommendations tailored to each user's interests, enticing users to buy relevant items. A common limitation of these emails is that the primary entry point, the title of the email, tends to follow fixed templates, failing to inspire enough interest in the contents. In this work, we explore the potential of large language models (LLMs) for generating thematic titles that reflect the personalized content of the emails. We perform offline simulations and conduct online experiments on the order of millions of users, finding our techniques useful in improving the engagement between customers and our emails. We highlight key findings and learnings as we productionize the safe and automated generation of email titles for millions of users.
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