Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)

Wenzheng Shu, Yanlong Huang, Wenxin Tai, Zhangtao Cheng, Bei Hui, Goce Trajcevski

AAAI 2024 pp. 23651-23652

doi:10.1609/AAAI.V38I21.30511 /aaai/2024/shu2024aaai-faithful/

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.

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Cite

Text

Shu et al. "Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30511

Markdown

[Shu et al. "Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/shu2024aaai-faithful/) doi:10.1609/AAAI.V38I21.30511

BibTeX

@inproceedings{shu2024aaai-faithful,
  title     = {{Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)}},
  author    = {Shu, Wenzheng and Huang, Yanlong and Tai, Wenxin and Cheng, Zhangtao and Hui, Bei and Trajcevski, Goce},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23651-23652},
  doi       = {10.1609/AAAI.V38I21.30511},
  url       = {https://mlanthology.org/aaai/2024/shu2024aaai-faithful/}
}