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（CTR）预测是在线推荐平台中的关键任务，其核心在于估计用户通过点击与广告或商品互动的概率。鉴于商业平台普遍提供在线购物、网约车、外卖及专业服务等多种服务，这些平台中的推荐系统需要跨多个领域而非单一领域进行点击率预测。然而，由于领域间复杂的相互影响，多领域点击率（MDCTR）预测仍是在线推荐中一项具有挑战性的任务。传统的MDCTR模型通常将领域编码为离散标识符，忽略了其背后丰富的语义信息，因此难以泛化至新领域。此外，现有模型易受某些特定领域主导，导致其他领域性能显著下降（即“跷跷板现象”）。本文提出一种新颖的解决方案Uni-CTR以应对上述挑战。Uni-CTR利用主干大型语言模型（LLM）学习能捕捉领域间共性的分层语义表示，同时采用多个领域专用网络以捕获各领域的特性。值得注意的是，我们设计了一种掩码损失策略，使这些领域专用网络与主干LLM解耦。该设计使得在新增或移除领域时，领域专用网络可保持不变，从而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显著优于当前最先进（SOTA）的MDCTR模型。此外，Uni-CTR在零样本预测中展现出卓越的有效性。我们已在工业场景中应用Uni-CTR，验证了其高效性。