查询性能预测器的理论分类

Victor Makarenkov, Bracha Shapira, L. Rokach
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引用次数: 5

摘要

查询性能预测任务的目的是在不获得用户相关性反馈的情况下估计查询的检索效率。最近提出的大多数预测因子都是用各种数据集进行经验评估的,以证明它们的优点。我们提出了一个框架,在没有经验评估的情况下对查询性能预测器(QPP)的价值进行理论分类和估计。我们展示了所提出的框架在四个代表性选择预测器上的应用,并展示了它如何强调它们的优势和劣势。本工作的主要贡献是基于理论的代表性QPP分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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