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Cross-view contrastive representation learning on meta-path induced graphs with node features for bundle

Peng Zhang1, Zhendong Niu2, Ru Ma3

  • 1organization=School of Computer Science and Technology, Beijing Institute of Technology, city=Beijing, country=China.

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|February 3, 2026
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Summary
This summary is machine-generated.

This study introduces a novel cross-view contrastive representation learning (CCRL) approach for bundle recommendation. CCRL effectively models complex user-bundle relationships, outperforming existing methods in suggesting correlated items.

Keywords:
Bundle recommendationContrastive learningGraph representation learningMeta-path induced graphsRecommendation systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Bundle recommendation suggests correlated items holistically, unlike item-by-item approaches.
  • Contrastive learning (CL) enhances node representations from item and bundle views for better recommendations.
  • Existing CL methods inadequately model user-user and bundle-bundle relationships and misuse graph structure for sample selection.

Purpose of the Study:

  • To address deficiencies in current bundle recommendation methods.
  • To propose a novel approach, cross-view contrastive representation learning (CCRL), for bundle recommendation.
  • To improve the modeling of high-order relationships and refine sample selection in contrastive learning.

Main Methods:

  • Introduced meta-path to construct meta-path induced graphs with node features, modeling user-user and bundle-bundle relationships from item and bundle views.
  • Performed graph representation learning on these graphs to obtain user and bundle representations.
  • Developed a novel contrastive loss supporting multiple positive samples for a cross-view graph CL mechanism to refine representations.
  • Trained the model using a joint optimization objective.

Main Results:

  • The proposed CCRL approach effectively models user-user and bundle-bundle relationships simultaneously.
  • The novel contrastive loss improves the selection of positive and negative samples by leveraging graph structure.
  • Experiments on benchmark datasets show that CCRL significantly outperforms existing baseline methods in bundle recommendation.

Conclusions:

  • The proposed CCRL method offers a superior approach to bundle recommendation by enhancing representation learning.
  • Explicitly modeling higher-order relationships and refining contrastive learning strategies leads to improved recommendation performance.
  • This work provides a strong foundation for future research in graph-based recommendation systems.