  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.
