ScholarWorks@중앙대학교

초록

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 F1 scores ranging from 0.163 to 0.305, and near-zero pairs-F1 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.

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