In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion rate (CVR). This results in the multi-type and multi-scenario CVR prediction problem. A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario. 2) Scalability: the model parameter size should be affordable. 3) Convenience: the model should not require a large amount of effort in data partitioning, subset processing and separate storage. Existing approaches cannot simultaneously satisfy these requirements. For example, building a separate model for each (conversion type, display scenario) pair is neither scalable nor convenient. Building a unified model trained on all the data with conversion type and display scenario included as two features is not accurate enough. In this paper, we propose the Masked Multi-domain Network (MMN) to solve this problem. To achieve the accuracy requirement, we model domain-specific parameters and propose a dynamically weighted loss to account for the loss scale imbalance issue within each mini-batch. To achieve the scalability requirement, we propose a parameter sharing and composition strategy to reduce model parameters from a product space to a sum space. To achieve the convenience requirement, we propose an auto-masking strategy which can take mixed data from all the domains as input. It avoids the overhead caused by data partitioning, individual processing and separate storage. Both offline and online experimental results validate the superiority of MMN for multi-type and multi-scenario CVR prediction. MMN is now the serving model for real-time CVR prediction in UC Toutiao.

中文翻译：

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屏蔽多域网络：单一模型多类型、多场景转化率预测

在现实世界的广告系统中，转化本质上有不同的类型，广告可以在不同的展示场景中展示，这两者都极大地影响了实际转化率（CVR）。这就导致了多类型、多场景的CVR预测问题。此问题所需的模型应满足以下要求： 1）准确性：模型应针对任何显示场景中的任何转换类型实现细粒度的准确性。 2）可扩展性：模型参数大小应该是可以承受的。 3）便利性：模型不需要在数据分区、子集处理和单独存储方面花费大量精力。现有方法无法同时满足这些要求。例如，为每个（转换类型、显示场景）对构建单独的模型既不具有可扩展性也不方便。构建一个对所有数据进行训练的统一模型，其中转换类型和显示场景作为两个特征是不够准确的。在本文中，我们提出了掩蔽多域网络（MMN）来解决这个问题。为了达到准确性要求，我们对特定领域的参数进行建模，并提出动态加权损失来解决每个小批量内的损失规模不平衡问题。为了实现可扩展性要求，我们提出了一种参数共享和组合策略，将模型参数从乘积空间减少到和空间。为了实现便利性要求，我们提出了一种自动屏蔽策略，可以将所有域的混合数据作为输入。避免了数据分区、单独处理、单独存储带来的开销。离线和在线实验结果都验证了MMN对于多类型、多场景CVR预测的优越性。 MMN现已成为UC今日头条实时CVR预测的服务模型。