An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-Ins
Abstract
Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.
Cite
Text
Zhang et al. "An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-Ins." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/491Markdown
[Zhang et al. "An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-Ins." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/zhang2020ijcai-interactive/) doi:10.24963/IJCAI.2020/491BibTeX
@inproceedings{zhang2020ijcai-interactive,
title = {{An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-Ins}},
author = {Zhang, Lu and Sun, Zhu and Zhang, Jie and Lei, Yu and Li, Chen and Wu, Ziqing and Kloeden, Horst and Klanner, Felix},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {3551-3557},
doi = {10.24963/IJCAI.2020/491},
url = {https://mlanthology.org/ijcai/2020/zhang2020ijcai-interactive/}
}