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，旨在估计用户通过点击广告或物品产生交互行为的概率。鉴于商业平台通常包含在线购物、网约车、外卖配送及专业服务等多种服务，其推荐系统需要跨多个域进行CTR预测，而非局限于单一域。然而，由于域间存在复杂的相互影响，多域点击率（MDCTR）预测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，导致难以泛化至新域。此外，现有模型易受特定域主导，造成其他域性能显著下降（即"跷跷板现象"）。本文提出创新方案Uni-CTR以应对上述挑战。Uni-CTR利用大语言模型（LLM）作为主干网络，学习跨域共性的层级语义表示，同时采用多个域专用网络捕获各域特性。我们设计了掩码损失策略，使域专用网络与主干LLM解耦，从而在新增或移除域时无需改动域专用网络，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表明，Uni-CTR显著优于当前最先进的MDCTR模型。此外，Uni-CTR在零样本预测中展现出卓越效果。我们已将其部署于工业场景，验证了实际应用效率。