Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them. Given the availability of various services like online shopping, ride-sharing, food delivery, and professional services on commercial platforms, recommendation systems in these platforms are required to make CTR predictions across multiple domains rather than just a single domain. However, multi-domain click-through rate (MDCTR) prediction remains a challenging task in online recommendation due to the complex mutual influence between domains. Traditional MDCTR models typically encode domains as discrete identifiers, ignoring rich semantic information underlying. Consequently, they can hardly generalize to new domains. Besides, existing models can be easily dominated by some specific domains, which results in significant performance drops in the other domains (i.e. the "seesaw phenomenon"). In this paper, we propose a novel solution Uni-CTR to address the above challenges. Uni-CTR leverages a backbone Large Language Model (LLM) to learn layer-wise semantic representations that capture commonalities between domains. Uni-CTR also uses several domain-specific networks to capture the characteristics of each domain. Note that we design a masked loss strategy so that these domain-specific networks are decoupled from backbone LLM. This allows domain-specific networks to remain unchanged when incorporating new or removing domains, thereby enhancing the flexibility and scalability of the system significantly. Experimental results on three public datasets show that Uni-CTR outperforms the state-of-the-art (SOTA) MDCTR models significantly. Furthermore, Uni-CTR demonstrates remarkable effectiveness in zero-shot prediction. We have applied Uni-CTR in industrial scenarios, confirming its efficiency.

翻译：点击率预测是在线推荐平台中的关键任务，旨在估计用户通过点击与广告或物品交互的概率。鉴于商业平台提供在线购物、网约车、外卖配送和专业服务等多种服务，其推荐系统需要跨多个域进行点击率预测，而非仅限单一域。然而，由于域间复杂的相互影响，多域点击率预测（MDCTR）仍是在线推荐中的挑战性任务。传统MDCTR模型通常将域编码为离散标识符，忽略了其蕴含的丰富语义信息，因此难以泛化到新域。此外，现有模型易被特定域主导，导致其他域性能显著下降（即"跷跷板现象"）。本文提出新解决方案Uni-CTR以应对上述挑战。Uni-CTR利用骨干大语言模型（LLM）学习逐层语义表示，捕捉域间共性；同时使用多个域专用网络捕获各域特性。特别地，我们设计了掩码损失策略，使域专用网络与骨干LLM解耦，从而在增删域时保持域专用网络不变，显著提升系统灵活性与可扩展性。在三个公开数据集上的实验结果表明，Uni-CTR显著优于现有最优（SOTA）MDCTR模型，且在零样本预测中展现出卓越效果。我们已在工业场景中部署Uni-CTR，验证了其高效性。