AAAI 2024technical0 citations
Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)
Wenzheng Shu, Yanlong Huang, Wenxin Tai, Zhangtao Cheng, Bei Hui, Goce Trajcevski
Abstract
Trip recommendation aims to plan user’s travel based on their specified preferences. Traditional heuristic and statistical approaches often fail to capture the intricate nuances of user intentions, leading to subpar performance. Recent deep-learning methods show attractive accuracy but struggle to generate faithful trajectories that match user intentions. In this work, we propose a DDPM-based incremental knowledge injection module to ensure the faithfulness of the generated trajectories. Experiments on two datasets verify the effectiveness of our approach.
BibTeX
@article{Shu_Huang_Tai_Cheng_Hui_Trajcevski_2024, title={Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30511}, DOI={10.1609/aaai.v38i21.30511}, abstractNote={Trip recommendation aims to plan user’s travel based on their specified preferences. Traditional heuristic and statistical approaches often fail to capture the intricate nuances of user intentions, leading to subpar performance. Recent deep-learning methods show attractive accuracy but struggle to generate faithful trajectories that match user intentions. In this work, we propose a DDPM-based incremental knowledge injection module to ensure the faithfulness of the generated trajectories. Experiments on two datasets verify the effectiveness of our approach.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shu, Wenzheng and Huang, Yanlong and Tai, Wenxin and Cheng, Zhangtao and Hui, Bei and Trajcevski, Goce}, year={2024}, month={Mar.}, pages={23651-23652} }