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Session interest model for CTR prediction based on feature co-action network.

Qianqian Wang^1,2, Fang'ai Liu³, Xiaohui Zhao³

¹School of Data and Computer Science, Shandong Women's University, Jinan, 250300, Shandong, P.R. China. wangqq_sdnu@163.com.

Scientific Reports

|April 25, 2025

Summary

This summary is machine-generated.

This study introduces the Session Interest Model with Feature Co-Action Network (SIFAN) to improve click-through rate (CTR) prediction accuracy by modeling user behavior within sessions and preserving feature interactions. SIFAN significantly outperforms existing models in predicting customer clicks.

Keywords:

Advertising Behavior sequence CTR Prediction model

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Area of Science:

Computer Science
Machine Learning
Artificial Intelligence

Background:

Click-through rate (CTR) prediction is crucial for advertising decisions.
Existing deep neural network models struggle to retain feature interaction power and model sequential user behavior effectively.
User behavior is session-based, a factor often overlooked in CTR prediction.

Purpose of the Study:

To propose a novel Session Interest Model with Feature Co-Action Network (SIFAN) for enhanced CTR prediction.
To effectively model user interest features while preserving the representational properties of feature interactions.
To address the challenge of session-based user behavior in CTR prediction.

Main Methods:

Employed a feature co-action network to capture interactions within individual customer behaviors.
Segmented sequential customer behavior into session layers.
Utilized gated recursive units (GRUs) with attention mechanisms to model sequential session interests and predict clicks.
Analyzed GRU attention update gates to determine correlations between session interests and target items.

Main Results:

The SIFAN model demonstrated significant performance advantages over other models under identical experimental conditions.
The model successfully captured implicit interactions and preserved original feature interactions.
Effective modeling of session-based user interests was achieved.

Conclusions:

SIFAN offers a superior approach to CTR prediction by integrating session-based modeling and feature interaction preservation.
The proposed model enhances the accuracy of predicting customer clicks in e-commerce and advertising.
Future research can explore further refinements of session-based modeling techniques for CTR prediction.