The official PyTorch implementation for our SIGIR'24 full paper Disentangled Contrastive Hypergraph Learning for Next POI Recommendation, which is accepted to SIGIR'2024 as full paper for oral presentation in Washington, U.S.
If you have any questions, please feel free to issue or contact me by email. If you use our codes and datasets in your research, please cite:
@inproceedings{lai2024disentangled,
title={Disentangled Contrastive Hypergraph Learning for Next POI Recommendation},
author={Lai, Yantong and Su, Yijun and Wei, Lingwei and He, Tianqi and Wang, Haitao and Chen, Gaode and Zha, Daren and Liu, Qiang and Wang, Xingxing},
booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={1452--1462},
year={2024}
}
Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user visiting behaviors and have achieved considerable performances. However, two key issues have received less attention: i) Most previous studies have ignored the fact that user preferences are diverse and constantly changing in terms of various aspects, leading to entangled and suboptimal user representations. ii) Many existing methods have inadequately modeled the crucial cooperative associations between different aspects, hindering the ability to capture complementary recommendation effects during the learning process. To tackle these challenges, we propose a novel framework Disentangled Contrastive Hypergraph Learning (DCHL) for next POI recommendation. Specifically, we design a multi-view disentangled hypergraph learning component to disentangle intrinsic aspects among collaborative, transitional and geographical views with adjusted hypergraph convolutional networks. Additionally, we propose an adaptive fusion method to integrate multi-view information automatically. Finally, cross-view contrastive learning is employed to capture cooperative associations among views and reinforce the quality of user and POI representations based on self-discrimination. Extensive experiments on three real-world datasets validate the superiority of our proposal over various state-of-the-arts.
python3 run.py --dataset NYC
python3 run.py --dataset TKY
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