Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)

Authors

  • Wenzheng Shu University of Electronic Science and Technology of China
  • Yanlong Huang University of Electronic Science and Technology of China
  • Wenxin Tai University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Zhangtao Cheng University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Bei Hui University of Electronic Science and Technology of China
  • Goce Trajcevski Iowa State University

DOI:

https://doi.org/10.1609/aaai.v38i21.30511

Keywords:

Data Mining, Knowledge Representation, Machine Learning

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.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Shu, W., Huang, Y., Tai, W., Cheng, Z., Hui, B., & Trajcevski, G. (2024). Faithful Trip Recommender Using Diffusion Guidance (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23651-23652. https://doi.org/10.1609/aaai.v38i21.30511

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program