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Operation-aware Neural Networks for user response prediction.

Yi Yang¹, Baile Xu¹, Shaofeng Shen¹

¹State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 30, 2019

Summary

Operation-aware Neural Networks (ONN) improve user response prediction by learning unique feature representations for different operations. This approach enhances performance in online advertising and recommendation systems.

Keywords:

Click-through rate prediction Factorization machines Neural networks

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Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

User response prediction is vital for online advertising and recommendation systems.
Learning feature interactions is key, with deep models automating high-order interactions.
High-dimensional sparse features in these systems necessitate effective representation learning.

Purpose of the Study:

To propose a novel neural network model, Operation-aware Neural Networks (ONN).
To address the limitation of shared representations in existing deep models.
To learn distinct representations tailored for different operations within the network.

Main Methods:

Developed Operation-aware Neural Networks (ONN).
ONN learns separate, operation-specific feature representations.
Evaluated ONN on large-scale real-world ad click/conversion datasets.

Main Results:

ONN consistently outperformed state-of-the-art models.
Demonstrated superior performance in both offline and online training environments.
Validated the effectiveness of operation-specific representation learning.

Conclusions:

Operation-aware Neural Networks offer a significant advancement in user response prediction.
Tailoring feature representations to specific operations improves model efficacy.
ONN provides a more effective deep learning approach for advertising and recommendation systems.