{"title":"RecSys挑战2015:具有分类特征的集成学习","authors":"Peter Romov, Evgeny Sokolov","doi":"10.1145/2813448.2813510","DOIUrl":null,"url":null,"abstract":"In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization based directly on the competition score. We describe our approach and discuss how it can be used to build scalable personalization systems.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"257 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"RecSys Challenge 2015: ensemble learning with categorical features\",\"authors\":\"Peter Romov, Evgeny Sokolov\",\"doi\":\"10.1145/2813448.2813510\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization based directly on the competition score. We describe our approach and discuss how it can be used to build scalable personalization systems.\",\"PeriodicalId\":324873,\"journal\":{\"name\":\"Proceedings of the 2015 International ACM Recommender Systems Challenge\",\"volume\":\"257 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2015 International ACM Recommender Systems Challenge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2813448.2813510\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 International ACM Recommender Systems Challenge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2813448.2813510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RecSys Challenge 2015: ensemble learning with categorical features
In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization based directly on the competition score. We describe our approach and discuss how it can be used to build scalable personalization systems.
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