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Sunbin Shin: Difference between revisions
→Conditional Generative Adversarial Network-Based Travel Route Recommendation
(Created page with "thumb == Education == * Mar 2020 - Feb. 2024: Sang-Myung University ** Bachelor's degree, Computer Science, Seoul, South Korea == Research Interests == * Generative AI * BigData == Research Experience == == Project == == Journal Publications == == Domestic Conference Papers == == Teaching ==") |
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== Education == | == Education == | ||
* Mar 2024 - Present: Chung-Ang University | |||
** M.S. Degree Knowledge Engineering, Computer Science & Engineering, Seoul, South Korea | |||
* Mar 2020 - Feb. 2024: Sang-Myung University | * Mar 2020 - Feb. 2024: Sang-Myung University | ||
** Bachelor's degree, Computer Science, Seoul, South Korea | ** Bachelor's degree, Computer Science & Engineering, Seoul, South Korea | ||
== Research Interests == | == Research Interests == | ||
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* BigData | * BigData | ||
== | == Project == | ||
* Aug. 2018 - Aug. 2018 | |||
** Hackathon in Ulsan - Scenery in Ulsan Taken with Drone | |||
* Jan. 2023 - Nov. 2023: Sang-Myung University | |||
** LSTM Stock Predict Application - iOS, Colab, NodeJS | |||
== Journal Publications == | == Journal Publications == | ||
=== '''Conditional Generative Adversarial Network-Based Travel Route Recommendation''' === | |||
Sunbin Shin, Luong Vuong Nguyen, Grzegorz J. Nalepa, Paulo Novais, Xuan Hau Pham, Jason J. Jung; ISSN=1546-2226; DOI:10.32604/cmc.2025.070613; | |||
Journal: Computers, Materials & Continua (Tech Science Press) | |||
https://doi.org/10.32604/cmc.2025.070613 | |||
"Recommending personalized travel routes from sparse, implicit feedback poses a significant challenge, as conventional systems often struggle with information overload and fail to capture the complex, sequential nature of user preferences. To address this, we propose a Conditional Generative Adversarial Network (CGAN) that generates diverse and highly relevant itineraries. Our approach begins by constructing a conditional vector that encapsulates a users unique generative capability, achieving scores ranging from 0.163 to 0.305, and near-zero scores between 0.002 and 0.022. These results confirm the model past itineraries. This work provides a robust solution for personalized travel planning, capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence." | |||