@Article{csse.2023.034712,
AUTHOR = {Bohuai Xiao, Xiaolan Xie, Chengyong Yang},
TITLE = {A Graph Neural Network Recommendation Based on Long- and Short-Term Preference},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {3},
PAGES = {3067--3082},
URL = {http://www.techscience.com/csse/v47n3/54566},
ISSN = {},
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.},
DOI = {10.32604/csse.2023.034712}
}