{"title":"为相似的观众打分","authors":"Qiang Ma, Eeshan Wagh, Jiayi Wen, Zhen Xia, Róbert Ormándi, Datong Chen","doi":"10.1109/ICDMW.2016.0097","DOIUrl":null,"url":null,"abstract":"Look-alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state-of-the-art look-alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity-based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real-world, large-scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look-alike models over methods which only use pairwise similarities of users.","PeriodicalId":373866,"journal":{"name":"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Score Look-Alike Audiences\",\"authors\":\"Qiang Ma, Eeshan Wagh, Jiayi Wen, Zhen Xia, Róbert Ormándi, Datong Chen\",\"doi\":\"10.1109/ICDMW.2016.0097\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Look-alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state-of-the-art look-alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity-based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real-world, large-scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look-alike models over methods which only use pairwise similarities of users.\",\"PeriodicalId\":373866,\"journal\":{\"name\":\"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2016.0097\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2016.0097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Look-alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state-of-the-art look-alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity-based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real-world, large-scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look-alike models over methods which only use pairwise similarities of users.