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Dynamic and Static Features-Aware Recommendation with Graph Neural Networks
Recommender systems are designed to deal with structured and unstructured information and help the user effectively retrieve needed information from the vast number of web pages. Dynamic information of users has been proven useful for learning representations in the recommender system. In this paper...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050307/ https://www.ncbi.nlm.nih.gov/pubmed/35498210 http://dx.doi.org/10.1155/2022/5484119 |
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author | Sun, Ninghua Chen, Tao Ran, Longya Guo, Wenshan |
author_facet | Sun, Ninghua Chen, Tao Ran, Longya Guo, Wenshan |
author_sort | Sun, Ninghua |
collection | PubMed |
description | Recommender systems are designed to deal with structured and unstructured information and help the user effectively retrieve needed information from the vast number of web pages. Dynamic information of users has been proven useful for learning representations in the recommender system. In this paper, we construct a series of dynamic subgraphs that include the user and item interaction pairs and the temporal information. Then, the dynamic features and the long- and short-term information of users are integrated into the static recommendation model. The proposed model is called dynamic and static features-aware graph recommendation, which can model unstructured graph information and structured tabular data. Particularly, two elaborately designed modules are available: dynamic preference learning and dynamic sequence learning modules. The former uses all user-item interactions and the last dynamic subgraph to model the dynamic interaction preference of the user. The latter captures the dynamic features of users and items by tracking the preference changes of users over time. Extensive experiments on two publicly available datasets show that the proposed model outperforms several compelling state-of-the-art baselines. |
format | Online Article Text |
id | pubmed-9050307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90503072022-04-29 Dynamic and Static Features-Aware Recommendation with Graph Neural Networks Sun, Ninghua Chen, Tao Ran, Longya Guo, Wenshan Comput Intell Neurosci Research Article Recommender systems are designed to deal with structured and unstructured information and help the user effectively retrieve needed information from the vast number of web pages. Dynamic information of users has been proven useful for learning representations in the recommender system. In this paper, we construct a series of dynamic subgraphs that include the user and item interaction pairs and the temporal information. Then, the dynamic features and the long- and short-term information of users are integrated into the static recommendation model. The proposed model is called dynamic and static features-aware graph recommendation, which can model unstructured graph information and structured tabular data. Particularly, two elaborately designed modules are available: dynamic preference learning and dynamic sequence learning modules. The former uses all user-item interactions and the last dynamic subgraph to model the dynamic interaction preference of the user. The latter captures the dynamic features of users and items by tracking the preference changes of users over time. Extensive experiments on two publicly available datasets show that the proposed model outperforms several compelling state-of-the-art baselines. Hindawi 2022-04-21 /pmc/articles/PMC9050307/ /pubmed/35498210 http://dx.doi.org/10.1155/2022/5484119 Text en Copyright © 2022 Ninghua Sun et al. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Research Article Sun, Ninghua Chen, Tao Ran, Longya Guo, Wenshan Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title | Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title_full | Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title_fullStr | Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title_full_unstemmed | Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title_short | Dynamic and Static Features-Aware Recommendation with Graph Neural Networks |
title_sort | dynamic and static features-aware recommendation with graph neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050307/ https://www.ncbi.nlm.nih.gov/pubmed/35498210 http://dx.doi.org/10.1155/2022/5484119 |
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