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TMH: Two-Tower Multi-Head Attention neural network for CTR prediction.

Zijian An¹, Inwhee Joe¹

¹Department of Computer Science, Hanyang University, Seoul, South Korea.

|March 15, 2024

Summary

This summary is machine-generated.

The Two-Tower Multi-Head Attention Neural Network (TMH) approach improves click-through rate (CTR) prediction by fusing explicit and implicit features. This method reduces reliance on manual feature engineering and enhances prediction accuracy.

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Area of Science:

Computational advertising
Machine learning for recommender systems
Information retrieval

Background:

Click-through rate (CTR) prediction is crucial in online advertising, driving ad relevance and revenue.
Increasing internet data volume escalates labor costs associated with traditional feature engineering.
Existing methods often struggle with effectively capturing complex feature interactions.

Purpose of the Study:

To propose a novel fusion model, the Two-Tower Multi-Head Attention Neural Network (TMH), for enhanced CTR prediction.
To reduce dependence on manual feature engineering by automatically learning feature combinations.
To improve prediction accuracy through higher-order explicit and implicit feature interactions.

Main Methods:

Developed an end-to-end TMH model integrating multi-head attention, residual networks, and deep neural networks.
The model learns vector-level combinations of explicit and implicit features.
Evaluated TMH performance using extensive experiments on three real-world datasets.

Main Results:

The TMH approach significantly outperformed existing CTR prediction methods across all tested datasets.
The model demonstrated effective fusion of explicit and implicit feature interactions.
Experimental results confirmed the superior predictive performance of the proposed TMH method.

Conclusions:

The TMH model offers a robust and interpretable solution for CTR prediction in large-scale advertising systems.
Automatic learning of feature interactions via TMH reduces the need for costly manual feature engineering.
The proposed approach represents a significant advancement in the field of CTR prediction.