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Contrastive Learning-Based Personalized Tag Recommendation.

Aoran Zhang1, Yonghong Yu2, Shenglong Li2

  • 1School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, China.

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|September 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel contrastive learning algorithm (CLPTR) to improve personalized tag recommendation by addressing data sparsity. CLPTR enhances user and item representations, achieving state-of-the-art performance in tag recommendation systems.

Keywords:
contrastive learninggraph neural networkpersonalized tag recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Personalized tag recommendation systems aim to provide users with relevant tags based on their preferences.
  • Traditional methods struggle with data sparsity, hindering accurate user, item, and tag embedding learning.
  • This limitation impacts the effectiveness of personalized tag recommendation models.

Purpose of the Study:

  • To propose a novel contrastive learning-based personalized tag recommendation algorithm (CLPTR).
  • To overcome the data sparsity problem in traditional tag recommendation systems.
  • To enhance the accuracy of learning user, item, and tag embeddings.

Main Methods:

  • Developed CLPTR, a contrastive learning algorithm for personalized tag recommendation.
  • Generated augmented user-tag and item-tag interaction graphs by injecting noise into implicit features.
  • Integrated contrastive learning with a graph neural network (GNN) model for self-supervised signal extraction.

Main Results:

  • CLPTR effectively preserves the semantics and structural information of interaction graphs.
  • The proposed method demonstrates state-of-the-art performance compared to traditional models.
  • Extensive experiments on real-world datasets validate the effectiveness of CLPTR.

Conclusions:

  • CLPTR offers a significant improvement over traditional personalized tag recommendation approaches.
  • The noise-injection strategy in graph augmentation is crucial for preserving data integrity.
  • This research advances the field of personalized recommendation systems through effective contrastive learning integration.