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

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

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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 brows-ing 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.

Browsing interest extractionspatial co-occurrencePoint-of-Interest(POI)semantic spaceword2vecPublic Map Service Platform(PMSP)

Guangsheng Dong、Rui Li、Huayi Wu、Wei Huang、Hongping Zhang、Vincent Tao、Quan Liu

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State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,China

Hubei Luojia Laboratory,Wuhan,China

Collaborative Innovation Center of Geospatial Technology,Wuhan University,Wuhan,China

National Geomatics Center of China,Beijing,China

School of Geomatics and Urban Spatial Information,Beijing University of Civil Engineering and Architecture,Beijing,China

Shanghai Weizhizhuoxin Information Technology Co.Ltd,Shanghai,China

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2024

10.1080/10095020.2022.2140078

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

影响因子：0.207
ISSN：1009-5020
年,卷(期)：2024.27(2)