{"title":"Criteo的大规模实时产品推荐","authors":"Romain Lerallut, Diane Gasselin, Nicolas Le Roux","doi":"10.1145/2792838.2799498","DOIUrl":null,"url":null,"abstract":"Performance retargeting consists of displaying online advertisements that are personalized according to each user's browsing history. We show close to three billion personalized ads a day, each of them optimized to generate the best post-click sales performance for our clients. Within this time frame, Criteo's recommender system must choose a dozen relevant products from billions of candidates in a few milliseconds. Our main challenge is to balance the amount of data we use with the processing speed and low-latency requirements of a web-scale environment.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Large-Scale Real-Time Product Recommendation at Criteo\",\"authors\":\"Romain Lerallut, Diane Gasselin, Nicolas Le Roux\",\"doi\":\"10.1145/2792838.2799498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Performance retargeting consists of displaying online advertisements that are personalized according to each user's browsing history. We show close to three billion personalized ads a day, each of them optimized to generate the best post-click sales performance for our clients. Within this time frame, Criteo's recommender system must choose a dozen relevant products from billions of candidates in a few milliseconds. Our main challenge is to balance the amount of data we use with the processing speed and low-latency requirements of a web-scale environment.\",\"PeriodicalId\":325637,\"journal\":{\"name\":\"Proceedings of the 9th ACM Conference on Recommender Systems\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2792838.2799498\",\"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 9th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2792838.2799498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Large-Scale Real-Time Product Recommendation at Criteo
Performance retargeting consists of displaying online advertisements that are personalized according to each user's browsing history. We show close to three billion personalized ads a day, each of them optimized to generate the best post-click sales performance for our clients. Within this time frame, Criteo's recommender system must choose a dozen relevant products from billions of candidates in a few milliseconds. Our main challenge is to balance the amount of data we use with the processing speed and low-latency requirements of a web-scale environment.
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