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Sunbin Shin: Difference between revisions
→Conditional Generative Adversarial Network-Based Travel Route Recommendation
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== Journal Publications == | == Journal Publications == | ||
=== '''Conditional Generative Adversarial Network-Based Travel Route Recommendation''' === | === '''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 | 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 | 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." | "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." | ||