Version 1
: Received: 10 February 2023 / Approved: 13 February 2023 / Online: 13 February 2023 (03:28:02 CET)
How to cite:
Patterson, D.; Wilson, J.; Forrow, M.; Wald, T. SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information. Preprints2023, 2023020196. https://doi.org/10.20944/preprints202302.0196.v1
Patterson, D.; Wilson, J.; Forrow, M.; Wald, T. SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information. Preprints 2023, 2023020196. https://doi.org/10.20944/preprints202302.0196.v1
Patterson, D.; Wilson, J.; Forrow, M.; Wald, T. SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information. Preprints2023, 2023020196. https://doi.org/10.20944/preprints202302.0196.v1
APA Style
Patterson, D., Wilson, J., Forrow, M., & Wald, T. (2023). SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information. Preprints. https://doi.org/10.20944/preprints202302.0196.v1
Chicago/Turabian Style
Patterson, D., Michael Forrow and Tereza Wald. 2023 "SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information" Preprints. https://doi.org/10.20944/preprints202302.0196.v1
Abstract
With the rapid development of the Internet industry, the problem of information overload has arisen due to the abundance of information available online. Recommendation algorithms, as the core of recommendation systems, have been attracting much attention and are a hot topic of research for many experts and scholars. The classical recommendation algorithms are mainly divided into three major categories: collaborative filtering recommendation algorithms, content-based recommendation algorithms, and hybrid-based recommendation algorithms. Although these algorithms are widely used in various fields, with the proliferation of information, these traditional recommendation algorithms are no longer able to meet the needs of the times. To address this issue, recommendation systems have been developed to provide users with personalized and relevant information or products. Despite the wide use of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, traditional recommendation algorithms have limitations and are no longer suitable for meeting the demands of the times. This paper proposes a new recommendation algorithm, SFRRG, that fuses structure and feature information in graph neural networks to improve the performance of the recommendation system in rating prediction. The effectiveness of the proposed algorithm is demonstrated through experiments on various data sets and compared with existing recommendation algorithms.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
