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DaGzang: a synthetic data generator for cross-domain recommendation services
Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280434/ https://www.ncbi.nlm.nih.gov/pubmed/37346525 http://dx.doi.org/10.7717/peerj-cs.1360 |
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author | Nguyen, Luong Vuong Vo, Nam D. Jung, Jason J. |
author_facet | Nguyen, Luong Vuong Vo, Nam D. Jung, Jason J. |
author_sort | Nguyen, Luong Vuong |
collection | PubMed |
description | Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon’s e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE). |
format | Online Article Text |
id | pubmed-10280434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804342023-06-21 DaGzang: a synthetic data generator for cross-domain recommendation services Nguyen, Luong Vuong Vo, Nam D. Jung, Jason J. PeerJ Comput Sci Artificial Intelligence Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon’s e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE). PeerJ Inc. 2023-05-02 /pmc/articles/PMC10280434/ /pubmed/37346525 http://dx.doi.org/10.7717/peerj-cs.1360 Text en ©2023 Nguyen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Nguyen, Luong Vuong Vo, Nam D. Jung, Jason J. DaGzang: a synthetic data generator for cross-domain recommendation services |
title | DaGzang: a synthetic data generator for cross-domain recommendation services |
title_full | DaGzang: a synthetic data generator for cross-domain recommendation services |
title_fullStr | DaGzang: a synthetic data generator for cross-domain recommendation services |
title_full_unstemmed | DaGzang: a synthetic data generator for cross-domain recommendation services |
title_short | DaGzang: a synthetic data generator for cross-domain recommendation services |
title_sort | dagzang: a synthetic data generator for cross-domain recommendation services |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280434/ https://www.ncbi.nlm.nih.gov/pubmed/37346525 http://dx.doi.org/10.7717/peerj-cs.1360 |
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