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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Knowledge-Enhanced Attentive Recommendation: A Graph Neural Network Approach for Context-Aware User Preference Modeling

Published 2024-11-19

  • Caiqian Cheng
    • ,
  • Lichao Zhu
    • ,
  • Xu Wang

Caiqian Cheng
Computer Science, University of California, San Diego, CA, USA

Lichao Zhu
Master Management science and engineering, Beijing Institute of Technology, Beijing, China

Xu Wang
Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

Abstract

This paper presents a knowledge-enhanced attentive recommendation framework that addresses critical challenges in personalized recommendation systems through the integration of knowledge graphs with graph neural networks. Traditional recommendation approaches face limitations in capturing complex user preferences and contextual factors, resulting in suboptimal performance particularly in sparse data and cold-start scenarios. We propose a novel architecture that leverages semantic knowledge to enrich user and item representations while employing a context-aware attention mechanism to dynamically weight information based on situational factors. The framework consists of three primary components: knowledge graph construction and embedding, graph neural network architecture for preference propagation, and context-aware attention mechanism for adaptive information aggregation. Comprehensive experiments on MovieLens-1M, Amazon-Book, and Yelp-2018 datasets demonstrate significant performance improvements, with our model achieving 7.2% higher precision and 9.1% better recall compared to state-of-the-art baselines. The approach demonstrates particular effectiveness in cold-start scenarios, with 27.8% improvement for users and items with minimal interaction history. Ablation studies confirm the substantial contribution of knowledge graph integration and attention mechanisms to recommendation quality. The framework maintains computational efficiency suitable for real-time applications while providing interpretable recommendations through traceable attention weights and semantic paths in the knowledge graph.

Keywords

Knowledge Graph, Graph Neural Networks, Context-Aware Recommendation, User Preference Modeling

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