Lerot:一个在线学习排名框架

Anne Schuth, Katja Hofmann, Shimon Whiteson, M. de Rijke
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引用次数: 49

摘要

在线学习对IR方法进行排名,允许检索系统通过点击反馈直接从与用户的交互中优化自己的性能。在本文中介绍的Lerot软件包中,我们将在线学习实验所需的所有成分捆绑在一起,以对IR进行排名。Lerot包括几种在线学习算法,交错方法和一整套评估这些方法的方法。在没有真实用户的情况下,软件包中捆绑的评估方法是基于用户与搜索引擎交互的模拟。在过去的几年中,这里介绍的软件已用于在主要信息检索场所验证超过六篇论文的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lerot: an online learning to rank framework
Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for IR. Lerot includes several online learning algorithms, interleaving methods and a full suite of ways to evaluate these methods. In the absence of real users, the evaluation method bundled in the software package is based on simulations of users interacting with the search engine. The software presented here has been used to verify findings of over six papers at major information retrieval venues over the last few years.
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