Grunnleggende konsepter
Proposing the Masked Multi-Domain Network (MMN) to address the multi-type and multi-scenario CVR prediction problem in advertising systems.
Sammendrag
The content introduces the problem of multi-type and multi-scenario CVR prediction in advertising systems. It discusses the challenges faced by existing approaches and presents the MMN model as a solution. The MMN model is designed to achieve accuracy, scalability, and convenience by incorporating domain-specific parameters, parameter sharing, auto-masking, and dynamically weighted loss. Experimental results demonstrate the superiority of MMN in multi-type and multi-scenario CVR prediction.
Introduction
CVR prediction is crucial in online advertising.
Different conversion types and display scenarios impact CVR.
Proposed Solution: Masked Multi-Domain Network (MMN)
MMN addresses the multi-type and multi-scenario CVR prediction problem.
Strategies include domain-specific parameters, parameter sharing, auto-masking, and dynamically weighted loss.
Experiments
Evaluation on News Feed and Criteo datasets.
Comparison with single-task, multi-task, and multi-domain methods.
MMN outperforms other methods in AUC, scalability, and convenience.
Ablation Studies
Effect of type-specific and scenario-specific parameters.
Effect of dynamically weighted loss.
Online Deployment
MMN deployed in an industrial news feed advertising system.
Achieved real-time CVR prediction with increased performance.
Statistikk
Building a unified model trained on all the data with conversion type and display scenario included as two features is not accurate enough.
MMN reduces the number of sets of domain-specific parameters from 357 to 39 in the Criteo dataset.
MMN reduces the number of separate datasets from 152 to 28 in the News Feed dataset.
Sitater
"In this paper, we propose the Masked Multi-domain Network (MMN) to solve this problem."
"MMN is now the serving model for real-time CVR prediction in UC Toutiao."