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CoHet4Rec: A recommendation for collaborative heterogeneous information networks.

Yao Chen1, Yuling Chen1, Zhi Ouyang1

  • 1State Key Laboratory of Public Big Data and College of Computer Science and Technology, Guizhou University, Guiyang, China.

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Summary
This summary is machine-generated.

This study introduces CoHet4Rec, a novel recommendation model that enhances accuracy by using Graph Neural Networks (GNNs) and a Collaborative Heterogeneous Information Network (CHIN). It effectively addresses the cold-start problem in recommender systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Recommender Systems (RS) traditionally use user-item interactions to predict preferences.
  • Graph Neural Networks (GNNs) improve RS by embedding graph data, but struggle with the cold-start problem.
  • Social recommendation leverages user connections, yet existing models need richer relationship exploration.

Purpose of the Study:

  • To propose CoHet4Rec, a novel recommendation model.
  • To address limitations of existing recommender systems, including data sparsity and the cold-start problem.
  • To enhance recommendation accuracy by incorporating diverse collaborative relationships beyond social networks.

Main Methods:

  • Developed CoHet4Rec, a recommendation model utilizing GNNs.
  • Constructed a Collaborative Heterogeneous Information Network (CHIN) with latent collaborative heterogeneous relation factors.
  • Employed factorized representations to capture diverse user-item connections and incorporate external knowledge.

Main Results:

  • CoHet4Rec demonstrated superior performance against 15 state-of-the-art (SOTA) recommendation techniques.
  • Achieved significant improvements in key metrics: up to 31.88% for HR@5 and 38.39% for NDCG@5.
  • Effectively alleviated data sparsity and the cold-start problem through enriched network information.

Conclusions:

  • CoHet4Rec offers a robust solution for enhancing recommender systems.
  • The model's flexibility allows for the integration of various knowledge sources.
  • This approach significantly improves recommendation quality by capturing complex user-item relationships.