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RAIN: Reconstructed-aware in-context enhancement with graph denoising for session-based recommendation.

Xinyi Zeng1, Shuchao Li2, Zequn Zhang2

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces RAIN, a novel method for session-based recommendation that effectively denoises both interaction graphs and user sessions. RAIN significantly improves recommendation accuracy by enhancing session representations and item embeddings.

Keywords:
Graph neural networksSelf-supervised learningSession denoisingSession-based recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Session-based recommendation systems predict user interests from short-term interactions.
  • Traditional methods struggle with noisy data (e.g., accidental clicks), leading to suboptimal session representations.
  • Existing approaches primarily focus on graph denoising for item embeddings, neglecting session-level noise.

Purpose of the Study:

  • To propose RAIN (Reconstructed-Aware In-context eNhancement with Graph Denoising), a novel model for session-based recommendation.
  • To address limitations in session representation learning caused by noisy interaction data.
  • To enhance recommendation accuracy by denoising both the interaction graph and the user session.

Main Methods:

  • RAIN employs a step-by-step denoising process for both the graph and session data.
  • Self-supervised signals guide edge clarity enhancement through masking and reconstruction.
  • An edge indicator is trained to identify and eliminate noisy edges, improving graph structure.
  • Reconstructed-aware in-context enhancement is integrated using a self-attentive mechanism and the trained edge indicator.

Main Results:

  • RAIN achieved significant improvements over state-of-the-art methods on four benchmark datasets.
  • Performance gains reached up to 7.05% in Hit@20 and 1.53% in MRR@20.
  • Experimental analysis validated the model's rationality and superiority in session-based recommendation.

Conclusions:

  • The proposed RAIN model effectively handles noisy data in session-based recommendation.
  • RAIN enhances both item embeddings and session representations for improved accuracy.
  • The method offers a superior approach to session-based recommendation compared to existing techniques.