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首页> 外文期刊>International journal of knowledge-based and intelligent engineering systems >Inferring document utility via a decision-making based retrieval model
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Inferring document utility via a decision-making based retrieval model

机译：通过基于决策的检索模型推断文档实用程序

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摘要

It is well known that a query is an approximate representation of the user's information needs since it does not provide a sufficient specification of the attended results. Numerous studies addressed this issue using techniques for better eliciting either document or query representations. More recent studies investigated the use of search context to better understand the user intent, driven by the query, in order to deliver personalized information results. In this article, we propose a personalized information retrieval model that leverages the information relevance by its usefulness to both the query and the user's profile, expressed by his main topics of interest. The model is based on the influence diagram formalism which is an extension of Bayesian networks dedicated to decision problems. This graphical model offers an intuitive way to represent, in the same framework, all the basic information (terms, documents, user interests) surrounding the user's information need and also, quantify their mutual influence on the relevance estimation. Experimental results demonstrate that our model was successful at eliciting user queries according to dynamic changes of the user interests.
机译：众所周知，查询是用户信息需求的一种近似表示，因为它没有提供足够的说明参加结果的规范。许多研究使用更好地引出文档或查询表示的技术来解决此问题。最近的研究调查了搜索上下文的使用，以便更好地理解由查询驱动的用户意图，以便提供个性化的信息结果。在本文中，我们提出了一种个性化的信息检索模型，该模型利用信息相关性通过其对查询和用户个人资料的有用性来发挥作用，并通过用户感兴趣的主要主题来表达。该模型基于影响图形式主义，它是专用于决策问题的贝叶斯网络的扩展。该图形模型提供了一种直观的方式，可以在同一框架中表示围绕用户信息需求的所有基本信息（术语，文档，用户兴趣），并量化其对相关性估计的相互影响。实验结果表明，我们的模型能够根据用户兴趣的动态变化成功引发用户查询。

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