KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Knowledge Recommendation Based on Dual Channel Hypergraph Convolution

  • Yue Li (Guangdong Communication Polytechnic, School of Information)
  • Received : 2022.12.06
  • Accepted : 2023.11.03
  • Published : 2023.11.30
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Abstract

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

Keywords

Acknowledgement

This work was supported by Department of Education of Guangdong Province under Grant No. GDGJ2021111 and the Chinese Society for Technical and Vocational Education under Grant No. ZJ2022B23.

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