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语义和协同信息检索系统中的隐式反馈学习
Implicit feedback learning in semantic and collaborative information retrieval systems |
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| 课程网址: |
http://videolectures.net/mcvc08_dupont_ifl/
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| 主讲教师: |
Gérard Dupont
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| 开课单位: |
欧洲宇航防务集团
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| 开课时间: |
2008-02-19
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| 课程语种: |
英语
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| 中文简介: |
信息检索是一个非常广泛的领域，它涉及各种类型的活动和任务。许多复杂的因素正在参与信息搜索，许多系统已经进行了试验。如今，围绕一个关键词/文档匹配过程已经形成了一个普遍共识，这个过程似乎在大规模上是有效的，并且具有足够的可靠性，能够满足大部分用户的需求。btu这个声明必须是有限的，对于某些主题，搜索仍然是一个困难的任务。解释这些现象的原因很多，但最突出的是用户在搜索信息时难以表达自己的需求，以及用户与信息检索系统之间共享知识的局限性，这意味着用户和机器都无法真正理解信息和知识。CE被另一个用作参考。本演示试图概述解决这些差距的一种方法：使用反馈学习。其目的是让系统学习用户行为，以便更好地定义其当前需求。机器学习算法在执行搜索时应用于来自用户的信号，可以导致理解与用户真正相关的内容，然后在执行任务时利用它来帮助用户。通过vitalas1项目，本文介绍了用户搜索日志的研究和反馈学习框架的定义。然后，对内隐相关反馈和查询优化进行了研究，作为开发反馈学习框架的第一次尝试。最后，概述了这些研究中的后续步骤，尤其是它们对VITALAS项目的影响。
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| 课程简介: |
Information retrieval is a very wide domain which can involve various types of activities and tasks. Many complex factors are participating in a search for information and many systems have been experimented. Nowadays a general consensus has been established around a keyword/document matching process which appears to be efficient on large scale and have enough reliability to satisfy a significant part of the users. Btu this claim has to be limited and for some subjects, search is still a difficult task. Many reasons can be proposed to explain these phenomena, but the most salient ones are the difficulty for users to express their needs while searching for information and the limitation of shared knowledge between users and information retrieval systems, meaning that both users and machines don't really understand the information and knowledge space used as references by the other. This presentation try to provide an overview of one way to resolve those gaps: using feedback learning. The aim is to make the system learning on user behaviour in order to better define its current needs. Machine learning algorithms applied on signal coming from user while performing a search can lead to the understanding of what is really relevant to the users and then can be exploited to help him during its tasks. The work, engaged through the VITALAS1 project, is presented: study of users search logs and definition of a feedback learning framework. Then research on implicit relevance feedback and query optimisation is presented as a first attempt to exploit the feedback learning framework. Finally an overview of the next steps within those studies is presented and especially their impact on the VITALAS project.
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| 关 键 词: |
信息检索; 搜索信息; 共享知识; 反馈学习框架; 隐式反馈
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-05-24：吴雨秋(课程编辑志愿者)
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| 阅读次数: |
57
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