Spatio-temporal intention learning for recommendation of next point-of-interest

ABSTRACT

Next point-of-interest (POI) recommendation has been applied by many internet companies to enhance the user travel experience. Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance. Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models. The preference pattern is different from the intention pattern, in that it does not emphasize the user mobility pattern of revisiting POIs, which is a common behavior and kind of intention for users. An effective module is needed to predict when and where users will repeat visits. In this paper, we propose a Spatio-Temporal Intention Learning Self-Attention Network (STILSAN) for next POI recommendation. STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time. Meanwhile, we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs. Experiments are conducted on two real-world check-in datasets. The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.

KEYWORDS:

• Point-of-Interest (POI)
• recommendation
• spatial pretrained model
• self-attention
• revisiting intention

Acknowledgements

The authors appreciate the efforts of the anonymous reviewers and the editor. The authors thank Jian Li, Kai Yan, and Fan Yu for their contributions to the revision of the manuscript. The authors thank Chongqing Changan Automobile Co., Ltd., Dongfeng Motor Corporation, and Dongfeng Changxing Tech. Co., Ltd. for their technical guidance

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings in this study, as well as the code to process it with the methods presented in this paper, are available on Github at https://github.com/leehommlee/STILSAN.

Additional information

Funding

This work is supported by Chongqing Technology Innovation and Application Development Project [grant number cstc2021jscx-dxwtBX0023], and funding from Chongqing Changan Automobile Co., Ltd., Dongfeng Motor Corporation, and Dongfeng Changxing Tech Co., Ltd.

Notes on contributors

Hao Li

Hao Li is a PhD candidate at the School of Remote Sensing and Information Engineering, Wuhan University. His research interest is POI recommendation and knowledge graph.

Peng Yue

Peng Yue is a Professor at the School of Remote Sensing and Information Engineering, Wuhan University. He serves as the deputy dean at the School of Remote Sensing and Information Engineering, the director at the Hubei Province Engineering Center for Intelligent Geoprocessing (HPECIG), and the director at the Institute of Geospatial Information and Location Based Services (IGILBS), Wuhan University. His research interests include Earth science data and information systems, Web GIS and GIServices, and GIS software and engineering.

Shangcheng Li

Shangcheng Li is an engineer at Dongfeng Changxing Tech. Co., Ltd. His research interest is travel data mining.

Chenxiao Zhang

Chenxiao Zhang is an associate research fellow in the School of Remote Sensing and Information Engineering at Wuhan University. He received a PhD from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research interests include geographic information system, remote sensing, and deep learning.

Can Yang

Can Yang is a postdoctoral researcher in the School of Remote Sensing and Information Engineering, Wuhan University. He received a PhD from KTH, the Royal Institute of Technology. His research interests include trajectory data mining and movement analysis.