Open Access

ARTICLE

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

by Bohuai Xiao^1,2, Xiaolan Xie^1,2,*, 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:

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

Received 25 July 2022; Accepted 11 October 2022; Issue published 09 November 2023

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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Keywords

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