{"title":"查询性能预测器的理论分类","authors":"Victor Makarenkov, Bracha Shapira, L. Rokach","doi":"10.1145/2808194.2809475","DOIUrl":null,"url":null,"abstract":"The query-performance prediction task aims at estimating the retrieval effectiveness of queries without obtaining relevance feedback from users. Most of the recently proposed predictors were empirically evaluated with various datasets to demonstrate their merits. We propose a framework for theoretical categorization and estimation of the value of query performance predictors (QPP) without empirical evaluation. We demonstrate the application of the proposed framework on four representative selected predictors and show how it emphasizes their strengths and weaknesses. The main contribution of this work is the theoretical grounded categorization of representative QPP.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Theoretical Categorization of Query Performance Predictors\",\"authors\":\"Victor Makarenkov, Bracha Shapira, L. Rokach\",\"doi\":\"10.1145/2808194.2809475\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The query-performance prediction task aims at estimating the retrieval effectiveness of queries without obtaining relevance feedback from users. Most of the recently proposed predictors were empirically evaluated with various datasets to demonstrate their merits. We propose a framework for theoretical categorization and estimation of the value of query performance predictors (QPP) without empirical evaluation. We demonstrate the application of the proposed framework on four representative selected predictors and show how it emphasizes their strengths and weaknesses. The main contribution of this work is the theoretical grounded categorization of representative QPP.\",\"PeriodicalId\":440325,\"journal\":{\"name\":\"Proceedings of the 2015 International Conference on The Theory of Information Retrieval\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"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/10.1145/2808194.2809475\",\"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 Conference on The Theory of Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2808194.2809475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Theoretical Categorization of Query Performance Predictors
The query-performance prediction task aims at estimating the retrieval effectiveness of queries without obtaining relevance feedback from users. Most of the recently proposed predictors were empirically evaluated with various datasets to demonstrate their merits. We propose a framework for theoretical categorization and estimation of the value of query performance predictors (QPP) without empirical evaluation. We demonstrate the application of the proposed framework on four representative selected predictors and show how it emphasizes their strengths and weaknesses. The main contribution of this work is the theoretical grounded categorization of representative QPP.