Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model

Konsep Inti

Proposing the Masked Multi-Domain Network (MMN) to address the multi-type and multi-scenario CVR prediction problem in advertising systems.

Abstrak

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.

Statistik

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.

Kutipan

"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."

Wawasan Utama Disaring Dari

Masked Multi-Domain Network

by Wentao Ouyan... pada arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17425.pdf

Pertanyaan yang Lebih Dalam

How can MMN's strategies be applied to other domains beyond advertising

The strategies employed by MMN, such as modeling domain-specific parameters, parameter sharing and composition, auto-masking, and dynamically weighted loss, can be applied to various domains beyond advertising. For example:

Healthcare: In healthcare, different patient demographics, medical conditions, and treatment options can be considered as different domains. By modeling domain-specific parameters, a model can provide personalized treatment recommendations based on individual patient characteristics.
Finance: In the finance sector, different financial products, customer segments, and market conditions can be treated as separate domains. By using parameter sharing and composition strategies, a model can optimize investment portfolios for different client profiles.
E-commerce: In e-commerce, different product categories, customer preferences, and sales channels can be viewed as distinct domains. By implementing auto-masking techniques, a model can tailor product recommendations and marketing strategies to specific customer segments.

What are the potential drawbacks of MMN's approach to multi-type and multi-scenario CVR prediction

While MMN's approach to multi-type and multi-scenario CVR prediction offers several advantages, there are potential drawbacks to consider:

Complexity: The implementation of MMN requires careful design and tuning of various strategies, which can increase the complexity of the model.
Data Sparsity: Handling a large number of conversion types and display scenarios may lead to data sparsity issues, especially if certain combinations have limited data points for training.
Computational Resources: Training a model with domain-specific parameters and auto-masking may require significant computational resources, especially for large-scale datasets.
Interpretability: The use of domain-specific parameters and complex strategies like auto-masking may make it challenging to interpret and explain the model's decisions.

How might the concept of domain-specific parameters be utilized in different machine learning applications

The concept of domain-specific parameters can be utilized in different machine learning applications to enhance model performance and flexibility:

Natural Language Processing (NLP): In sentiment analysis, different domains such as social media, product reviews, and news articles can have unique language patterns. By incorporating domain-specific parameters, a sentiment analysis model can better capture the nuances of each domain.
Image Recognition: In image classification tasks, different image datasets representing various categories (e.g., animals, vehicles, landscapes) can be treated as separate domains. Domain-specific parameters can help the model focus on distinguishing features within each category.
Recommendation Systems: In recommendation systems, different user preferences, item categories, and interaction types can be considered as distinct domains. By using domain-specific parameters, a recommendation model can provide personalized suggestions based on specific user-item interactions.