|
|
| (5 intermediate revisions by the same user not shown) |
| Line 13: |
Line 13: |
| * Generative AI | | * Generative AI |
| * BigData | | * BigData |
|
| |
| == Research Experience ==
| |
|
| |
|
| == Project == | | == Project == |
| Line 21: |
Line 19: |
| ** Hackathon in Ulsan - Scenery in Ulsan Taken with Drone | | ** Hackathon in Ulsan - Scenery in Ulsan Taken with Drone |
|
| |
|
| * Jan. 2023 - Nov. 2023: SangMyung University | | * Jan. 2023 - Nov. 2023: Sang-Myung University |
| ** LSTM Stock Predict Application - iOS, Colab, NodeJS | | ** LSTM Stock Predict Application - iOS, Colab, NodeJS |
|
| |
|
| == Journal Publications == | | == Journal Publications == |
| https://doi.org/10.32604/cmc.2025.070613
| | === '''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; |
|
| |
|
| === '''Conditional Generative Adversarial Network-Based Travel Route Recommendation''' ===
| | Journal: Computers, Materials & Continua (Tech Science Press) |
| "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 user’s profile. This vector uniquely fuses embeddings from a Heterogeneous Information Network (HIN) to model complex user-place-route relationships, a Recurrent Neural Network (RNN) to capture sequential path dynamics, and Neural Collaborative Filtering (NCF) to incorporate collaborative signals from the wider user base. This comprehensive condition, further enhanced with features representing user interaction confidence and uncertainty, steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations, effectively mitigating the data sparsity problem. Recommendations are then formulated using an Anchor-and-Expand algorithm, which selects relevant starting Points of Interest (POI) based on user history, then expands routes through latent similarity matching and geographic coherence optimization, culminating in Traveling Salesman Problem (TSP)-based route optimization for practical travel distances. Experiments on a real-world check-in dataset validate our model’s 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’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’ 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."
| |
|
| |
|
| == Domestic Conference Papers ==
| | https://doi.org/10.32604/cmc.2025.070613 |
|
| |
|
| == Teaching ==
| | "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." |
