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 o...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,highfrequency 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.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers:U20A209141771426]Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence[grant number ZZJJ202204]LIESMARS Special Research Funding.
文摘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,highfrequency 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.
