MIMA: Multi-Feature Interaction Meta-Path Aggregation Heterogeneous Graph Neural Network for Recommendations

Yang Li (), Shichao Yan, Fangtao Zhao, Yi Jiang, Shuai Chen, Lei Wang and Li Ma
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Yang Li: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China
Shichao Yan: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China
Fangtao Zhao: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China
Yi Jiang: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China
Shuai Chen: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China
Lei Wang: Data Processing Center, Henan Provincial Bureau of Statistics, Zhengzhou 450016, China
Li Ma: College of Computer Science and Technology, North China University of Technology, Shijingshan, Beijing 100144, China

Future Internet, 2024, vol. 16, issue 8, 1-20

Abstract: Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for learning node embeddings, failing to leverage attribute information fully and to clarify the reasons behind a user’s interest in specific items. A heterogeneous graph neural network for recommendation named MIMA (multi-feature interaction meta-path aggregation) is proposed to address these issues. Firstly, heterogeneous graphs consisting of user nodes, item nodes, and their feature nodes are constructed, and the meta-path containing users, items, and their attribute information is used to capture the correlations among different types of nodes. Secondly, MIMA integrates attention-based feature interaction and meta-path information aggregation to uncover structural and semantic information. Then, the constructed meta-path information is subjected to neighborhood aggregation through graph convolution to acquire the correlations between different types of nodes and to further facilitate high-order feature fusion. Furthermore, user and item embedding vector representations are obtained through multiple iterations. Finally, the effectiveness and interpretability of the proposed approach are validated on three publicly available datasets in terms of NDCG, precision, and recall and are compared to all baselines.

Keywords: heterogeneous graph; multi-head attention; multi-feature interaction; meta-path aggregation; heterogeneous graph neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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