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Session interest model for CTR prediction based on self-attention mechanism.

Qianqian Wang1,2, Fang'ai Liu3, Xiaohui Zhao4

  • 1Shandong Women's University, Jinan, China.

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This study introduces the Session Interest Model via Self-Attention (SISA) for click-through rate (CTR) prediction. SISA effectively models user interests by considering session-based behavior, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Click-through rate (CTR) prediction is crucial for online advertising.
  • Capturing evolving user interests from behavior sequences is a key challenge.
  • Existing models often overlook the session-based nature of user behavior.

Purpose of the Study:

  • To propose an effective model for CTR prediction that accounts for user behavior within sessions.
  • To address the limitations of existing models in handling session-based user interests.

Main Methods:

  • The Session Interest Model via Self-Attention (SISA) was developed.
  • User behavior sequences are divided into sessions.
  • Self-attention with bias coding models individual sessions.
  • Gated Recurrent Unit (GRU) captures inter-session dynamics.

Main Results:

  • The SISA model demonstrates superior performance compared to other existing models.
  • The model effectively captures correlations within sessions and interactions across sessions.

Conclusions:

  • SISA provides an effective approach to CTR prediction by modeling session-based user interests.
  • The proposed method enhances the accuracy of predicting user clicks in online advertising.