Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Minsung Hong"'
Publikováno v:
Sensors, Vol 20, Iss 9, p 2510 (2020)
The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we fo
Externí odkaz:
https://doaj.org/article/db48d82611a2490e9ccc5c3194e845b0
Publikováno v:
Journal of Tourism Sciences. 46:121-141
Publikováno v:
The Journal of Society for e-Business Studies. 27:67-85
Autor:
Minsung Hong, Jason J. Jung
Publikováno v:
Applied Intelligence. 52:15006-15025
Publikováno v:
International Journal of Data Science and Analytics.
Autor:
Elena Romanovskaia, Ho Lun Chan, Valentin Romanovski, Francisco Garfias, Minsung Hong, Sara Mastromarino, Peter Hosemann, Raluca Scarlat, John R. Scully
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af4005de8ba674b231bb369dca506620
https://doi.org/10.2139/ssrn.4435940
https://doi.org/10.2139/ssrn.4435940
Autor:
Minsung Hong
Publikováno v:
Information Sciences. 562:259-278
In the field of recommender systems, diversity as the measure of recommendation quality has gained much attention recently. Unfortunately, many researchers have shown that it has a trade-off relation with accuracy. Meanwhile, tensor factorization has
Publikováno v:
Computer Science & Information Systems; Jun2023, Vol. 20 Issue 3, p911-931, 21p
Publikováno v:
Corrosion Science. 212:110913
Autor:
Jason J. Jung, Minsung Hong
Publikováno v:
Journal of Ambient Intelligence and Smart Environments. 13:5-19
Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simu