Learning the spatial co-occurrence for browsing interests extraction of domain users on public map service platforms

ABSTRACT

Public Map Service Platforms (PMSPs) provide embedded map services in domains such as forests and rivers. Users from different domains (Domain Users) prefer specific spatial features, and extracting the Browsing Interests of Domain Users (BIDUs) can help elucidate users’ access intentions and provide suitable recommendations. Previous research has found that access frequency of spatial features is an indicator of users’ browsing interests; however, high-frequency spatial features are sparsely distributed, resulting in inaccurate extraction of browsing interests. Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation. First, to extract spatial features in tiles, we proposed a k-nearest neighbor method for Point-of-Interest (POI) extraction and a template-based method for Land Uses/Land Covers extraction. Then, we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness. Finally, a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs. Compared with the baseline models, the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction. Our findings can help construct domain user profiles and support the development of intelligent PMSPs.

KEYWORDS:

• Browsing interest extraction
• spatial co-occurrence
• Point-of-Interest (POI) semantic space
• word2vec
• Public Map Service Platform (PMSP)

Acknowledgments

The authors thank the National Geomatics Center of China and Tianditu for supporting this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this paper was collected by the National Geomatics Center of China and Tianditu. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly for protecting the users’ privacy. The logs from OpenStreetMap could become the alternative (https://planet.openstreetmap.org/tile_logs/).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers: U20A2091 41771426], Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence [grant number ZZJJ202204], and LIESMARS Special Research Funding.

Notes on contributors

Guangsheng Dong

Guangsheng Dong received BS degree from Central South University, China, in 2015 and PhD degree from Wuhan University, China, in 2021. Currently, he is a post-doc and focuses on spatial-temporal data mining and intelligent geographic information service.

Rui Li

Rui Li is currently a full professor in the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. Her scientific interests include networking communication, spatial-temporal computing, and networks GIS.

Huayi Wu

Huayi Wu is a full professor in GeoInformatics and the Vice Director of the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His scientific interests include high-performance geospatial computing and intelligent geospatial web services.

Wei Huang

Wei Huang is the Director of the public platform department in the National Geomatics Center of China. His scientific interests are the development of the public service platform of geographical information and spatial cloud computing.

Hongping Zhang

Hongping Zhang is a senior engineer in the department of the public platform in the National Geomatics Center of China, majoring in GIS web service and integrated application.

Vincent Tao

Vincent Tao, male, born in November 1967, is a Canadian. In 1998, he received his doctor’s degree in geomatics from the University of Calgary, Canada, the founder and CEO of Wayz.ai. and a high-level overseas talent in Shanghai. Mainly engaged in the research of spatio-temporal artificial intelligence technology. An entrepreneurial entrepreneur recognized by the industry as having both business operation and in-depth technical skills, and an authoritative expert in the international map industry, he has made a number of technological inventions, published more than 200 papers, and was awarded as a tenured professor of York University and the national chief professor of space information in Canada.

Quan Liu

Quan Liu received his Master’s degree of Engineering from University of California, Los Angeles in 2018. Currently he is Vice President of Engineering at Wayz.ai. His research interest includes knowledge graph, spatio-temporal AI and big data.