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Updated: Sep 1, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Published on: May 13, 2022

A CTR prediction model based on session interest.

Qianqian Wang¹, Fang'ai Liu², Xiaohui Zhao²

¹Shandong Women's University, Jinan, China.

|August 17, 2022

Summary

This summary is machine-generated.

This study introduces a hierarchical model for click-through rate (CTR) prediction, focusing on user session interests. The proposed Session Interest Hierarchical Model (SIHM) improves CTR prediction accuracy by analyzing sequential user behaviors within and across sessions.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Click-through rate (CTR) prediction is crucial for online advertising effectiveness.
Existing CTR models often overlook the session-based structure of user behavior, where intra-session actions are correlated but inter-session relevance is limited.

Purpose of the Study:

To propose a novel hierarchical model, the Session Interest Hierarchical Model (SIHM), for CTR prediction.
To effectively model user multiple session interests by considering both intra-session and inter-session dynamics.

Main Methods:

User sequential behavior is segmented into a session layer.
A self-attention network is utilized to derive session interest representations.
Bidirectional Long Short-Term Memory (BLSTM) network captures interactions between different session interests.
An attention-based LSTM (A-LSTM) aggregates session interests to predict target ad influence.

Main Results:

The proposed SIHM model demonstrates superior performance compared to existing CTR prediction models.
The hierarchical approach effectively captures complex user behavior patterns across multiple sessions.

Conclusions:

The SIHM model offers a significant advancement in CTR prediction by incorporating session-aware user interest modeling.
This approach enhances the accuracy and relevance of advertising targeting.