A recommendation model with multi-scale semantic fusion on heterogeneous information network

Haoxiang Zhang; Xiaoru Wang; Xiaolong Li; Jiabin Zhang

doi:10.1117/12.2684834

21 July 2023 A recommendation model with multi-scale semantic fusion on heterogeneous information network

Haoxiang Zhang, Xiaoru Wang, Xiaolong Li, Jiabin Zhang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271735 (2023) https://doi.org/10.1117/12.2684834
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Recommendation system aims to provide effective and personalized recommendation for users and solve the problem of information overload. However, the existing recommendation system lack of effective utilization of heterogeneous data and have the problem of information loss in the process of semantic information fusion. In this paper, we propose a Multi-scale Semantic Fusion Recommendation model (MSFRec) based on heterogeneous information networks to solve the problems above. First, we use heterogeneous graph and metapath to describe the complex semantic structure in the recommendation tasks. Then we divide the neighborhood guided by metapath into multiple layers and use the multi-layer interaction to capture multi-scale semantic information. Finally, we use a two-stage relational attention to guide the multi-scale semantic fusion. Extensive experiments show that MSFRec achieves competitive results in recommendation tasks.

Citation Download Citation

Haoxiang Zhang, Xiaoru Wang, Xiaolong Li, and Jiabin Zhang "A recommendation model with multi-scale semantic fusion on heterogeneous information network", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271735 (21 July 2023); https://doi.org/10.1117/12.2684834

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KEYWORDS

Semantics

Information fusion

Design and modelling

Ablation

Neural networks

Performance modeling

Convolution

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