{"title":"Condensed List Relevance Models","authors":"Fernando Diaz","doi":"10.1145/2808194.2809491","DOIUrl":null,"url":null,"abstract":"Pseudo-relevance feedback has traditionally been implemented as an expensive re-retrieval of documents from the target corpus. In this work, we demonstrate that, for high precision metrics, re-ranking the original feedback set provides nearly identical performance to re-retrieval with significantly lower latency.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"201 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2808194.2809491","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Pseudo-relevance feedback has traditionally been implemented as an expensive re-retrieval of documents from the target corpus. In this work, we demonstrate that, for high precision metrics, re-ranking the original feedback set provides nearly identical performance to re-retrieval with significantly lower latency.
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