Https://arxiv.org/pd F/2011.05061.pdf

2011.05061.pdf
Preview of https://arxiv.org/pd f/2011.05061.pdf
🔗 Source: arxiv.org
📊 Size: 1.42 MB
📄 Pages: 10 pages
⬇️ Downloads: 64

Summary

The authors propose a knowledge graph-aware recommender system, KGPL, that leverages pseudo-labelling and graph neural networks to alleviate cold-start problems in recommendation. Unlike conventional methods that handle unobserved samples as negative instances, KGPL assigns pseudo-labels to unobserved samples based on model predictions, allowing them to be handled as weak-positive instances. The system uses two sampling strategies: KG-aware sampling for pseudo-labelling and popularity-aware sampling for negative instances. Experimental results demonstrate the effectiveness of KGPL in improving recommendation performance for cold-start users and items.

Description

The authors propose a knowledge graph-aware recommender system, KGPL, that leverages pseudo-labelling and graph neural networks to alleviate cold-start...

Technical Information

  • File Format: PDF
  • File Size: 1.42 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 64
  • Last Updated: 1 month ago

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