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Graph neural network recommendation algorithm based on improved dual tower model.

Qiang He1, Xinkai Li2, Biao Cai3,4

  • 1School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China. heqiang@cdut.edu.cn.

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Summary
This summary is machine-generated.

This study introduces the interactive higher-order dual tower (IHDT) model to enhance recommendation systems. IHDT improves content discovery accuracy and recall by incorporating interactive and higher-order feature learning.

Keywords:
Collaborative filteringDual tower modelGraph neural networkRecommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Recommendation systems are crucial for navigating information overload.
  • Current systems face challenges in balancing recall breadth with accuracy.
  • Dual tower models are common but can be enhanced.

Purpose of the Study:

  • To propose a novel recommendation algorithm, the interactive higher-order dual tower (IHDT).
  • To improve the accuracy and recall of content recommendations.
  • To introduce interactivity and higher-order feature learning into dual tower models.

Main Methods:

  • Constructing a heterogeneous graph with users, items, and attributes.
  • Utilizing meta-paths for richer feature extraction.
  • Implementing an interactive learning mechanism for feature injection between towers.
  • Employing graph convolutional networks (GCNs) for higher-order feature learning.
  • Pooling node embeddings for enhanced user and item representations.

Main Results:

  • The IHDT model demonstrated superior performance compared to baseline methods on the MovieLens dataset.
  • Ablation experiments confirmed the effectiveness of the interactive learning and high-order GCN components.
  • The proposed model achieved a better balance between recommendation accuracy and recall.

Conclusions:

  • The IHDT model offers a significant advancement in recommendation system design.
  • Interactive learning and higher-order GCNs are key contributors to improved performance.
  • This approach effectively addresses the challenge of information explosion in content discovery.