{"title":"通过集成学习挖掘IP足迹恢复跨设备连接","authors":"Xuezhi Cao, Weiyue Huang, Yong Yu","doi":"10.1109/ICDMW.2015.129","DOIUrl":null,"url":null,"abstract":"This paper describes our solution to ICDM 2015's contest. The challenge is to recover cross-device connections, i.e. identifying device-cookie pairs that is used by the same natural person. To tackle this task, we first model the privateness of each IP, then employ pairwise ranking techniques for predicting the likelihood of each connection, finally ensemble learning is used for integrating multiple models from various settings. Our approach achieves 5th place in the contest (average F-score of 0.8608) using ONLY IP footprint information.","PeriodicalId":192888,"journal":{"name":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Recovering Cross-Device Connections via Mining IP Footprints with Ensemble Learning\",\"authors\":\"Xuezhi Cao, Weiyue Huang, Yong Yu\",\"doi\":\"10.1109/ICDMW.2015.129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our solution to ICDM 2015's contest. The challenge is to recover cross-device connections, i.e. identifying device-cookie pairs that is used by the same natural person. To tackle this task, we first model the privateness of each IP, then employ pairwise ranking techniques for predicting the likelihood of each connection, finally ensemble learning is used for integrating multiple models from various settings. Our approach achieves 5th place in the contest (average F-score of 0.8608) using ONLY IP footprint information.\",\"PeriodicalId\":192888,\"journal\":{\"name\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2015.129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2015.129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recovering Cross-Device Connections via Mining IP Footprints with Ensemble Learning
This paper describes our solution to ICDM 2015's contest. The challenge is to recover cross-device connections, i.e. identifying device-cookie pairs that is used by the same natural person. To tackle this task, we first model the privateness of each IP, then employ pairwise ranking techniques for predicting the likelihood of each connection, finally ensemble learning is used for integrating multiple models from various settings. Our approach achieves 5th place in the contest (average F-score of 0.8608) using ONLY IP footprint information.
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