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 (\ie 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（CTR）预测是在线推荐平台中的关键任务，因其涉及估计用户点击广告或物品的概率。鉴于商业平台上存在在线购物、拼车、外卖及专业服务等多种服务，这些平台中的推荐系统需跨多个领域而非单一领域进行CTR预测。然而，多领域点击率（MDCTR）预测因领域间复杂的相互影响而仍是一项具有挑战性的任务。传统MDCTR模型通常将领域编码为离散标识符，忽略了其背后的丰富语义信息，因此难以泛化至新领域。此外，现有模型易被特定领域主导，导致其他领域性能显著下降（即“跷跷板现象”）。本文提出新型解决方案Uni-CTR以应对上述挑战。Uni-CTR利用骨干大语言模型（LLM）学习分层语义表示，捕捉领域间的共性，同时使用多个领域特定网络捕捉各领域特性。需注意，我们设计了掩码损失策略，使领域特定网络与骨干LLM解耦，从而在新增或移除领域时保持领域特定网络不变，显著提升系统的灵活性与可扩展性。在三个公开数据集上的实验结果表明，Uni-CTR显著优于最先进（SOTA）的MDCTR模型。此外，Uni-CTR在零样本预测中展现出卓越有效性。我们已在工业场景中部署Uni-CTR，验证了其高效性。