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A Graph Neural Network Recommendation Based on Long- and Short-Term Preference

Bohuai Xiao1,2, Xiaolan Xie1,2,*, Chengyong Yang3

1 School of Information Science and Engineering, Guilin University of Technology, Guilin, 541004, China
2 Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, 541004, China
3 Network and Information Center, Guilin University of Technology, Guilin, 541004, China

* Corresponding Author: Xiaolan Xie. Email: email

Computer Systems Science and Engineering 2023, 47(3), 3067-3082. https://doi.org/10.32604/csse.2023.034712

Abstract

The recommendation system (RS) on the strength of Graph Neural Networks (GNN) perceives a user-item interaction graph after collecting all items the user has interacted with. Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession. However, user preferences are dynamic. With the passage of time and some trend guidance, users may generate some short-term preferences, which are more likely to lead to user-item interactions. A GNN recommendation based on long- and short-term preference (LSGNN) is proposed to address the above problems. LSGNN consists of four modules, using a GNN combined with the attention mechanism to extract long-term preference features, using Bidirectional Encoder Representation from Transformers (BERT) and the attention mechanism combined with Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract short-term preference features, using Convolutional Neural Network (CNN) combined with the attention mechanism to add title and description representations of items, finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations. In experiments conducted on five publicly available datasets from Amazon, LSGNN is superior to state-of-the-art personalized recommendation techniques.

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APA Style
Xiao, B., Xie, X., Yang, C. (2023). A graph neural network recommendation based on long- and short-term preference. Computer Systems Science and Engineering, 47(3), 3067-3082. https://doi.org/10.32604/csse.2023.034712
Vancouver Style
Xiao B, Xie X, Yang C. A graph neural network recommendation based on long- and short-term preference. Comput Syst Sci Eng. 2023;47(3):3067-3082 https://doi.org/10.32604/csse.2023.034712
IEEE Style
B. Xiao, X. Xie, and C. Yang "A Graph Neural Network Recommendation Based on Long- and Short-Term Preference," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 3067-3082. 2023. https://doi.org/10.32604/csse.2023.034712



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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