{"title":"基于分配问题的GPS数据查找相似用户","authors":"Zedong Lin, Q. Zeng, H. Duan, Faming Lu","doi":"10.1145/3290420.3290470","DOIUrl":null,"url":null,"abstract":"The application of location-aware technology has increasingly accumulated a large amount of user spatiotemporal data, which gives us the opportunity to analyze users' interests and behavior patterns. This paper proposes a new method for constructing user's mobility profiles and measuring their similarity. First, a new method of using the categories of points of interest (POI) to represent the semantics of a geographical area (stay region) where a user stays for a long period of time is proposed. Second, the sequential pattern mining technique is applied to extract the user's mobility profiles. Then, the user similarity is computed based on the maximum benefit assignment problem. Finally, a comparison experiment with the existing works on the real dataset verifies the effectiveness of the proposed approach.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"380 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Finding similar users from GPS data based on assignment problem\",\"authors\":\"Zedong Lin, Q. Zeng, H. Duan, Faming Lu\",\"doi\":\"10.1145/3290420.3290470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application of location-aware technology has increasingly accumulated a large amount of user spatiotemporal data, which gives us the opportunity to analyze users' interests and behavior patterns. This paper proposes a new method for constructing user's mobility profiles and measuring their similarity. First, a new method of using the categories of points of interest (POI) to represent the semantics of a geographical area (stay region) where a user stays for a long period of time is proposed. Second, the sequential pattern mining technique is applied to extract the user's mobility profiles. Then, the user similarity is computed based on the maximum benefit assignment problem. Finally, a comparison experiment with the existing works on the real dataset verifies the effectiveness of the proposed approach.\",\"PeriodicalId\":259201,\"journal\":{\"name\":\"International Conference on Critical Infrastructure Protection\",\"volume\":\"380 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Critical Infrastructure Protection\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3290420.3290470\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Critical Infrastructure Protection","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3290420.3290470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Finding similar users from GPS data based on assignment problem
The application of location-aware technology has increasingly accumulated a large amount of user spatiotemporal data, which gives us the opportunity to analyze users' interests and behavior patterns. This paper proposes a new method for constructing user's mobility profiles and measuring their similarity. First, a new method of using the categories of points of interest (POI) to represent the semantics of a geographical area (stay region) where a user stays for a long period of time is proposed. Second, the sequential pattern mining technique is applied to extract the user's mobility profiles. Then, the user similarity is computed based on the maximum benefit assignment problem. Finally, a comparison experiment with the existing works on the real dataset verifies the effectiveness of the proposed approach.