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首页> 外文会议>65th ASIST(American Society for Information Science and Technology) Annual Meeting, Nov 18-21, 2002, Philadelphia, PA >Weighted Markov Chains and Graphic State Nodes for Information Retrieval
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Weighted Markov Chains and Graphic State Nodes for Information Retrieval

机译：信息检索的加权马尔可夫链和图形状态节点

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摘要

Decision-making in uncertain environments, such as data mining, involves a computer user navigating through multiple steps, from initial submission of a query through evaluating retrieval results, determining degrees of acceptability of the results, and advancing to a terminal state of evaluating where the interaction is successful or not. This paper describes iterative information seeking (IS) as a Markov process during which users advance through states of "nodes". Nodes are graphic objects on a computer screen that represent both the state of the system and the group of users' or an individual user's degree of confidence in an individual node. After examining nodes to establish a confidence level, the system records the decision as weights affecting the probability of the transition paths between nodes. By training the system in this way, the model incorporates into the underlying Markov process users' decisions as a means to reduce uncertainty. The Markov chain becomes a weighted one whereby the IS makes justified suggestions.
机译：在不确定的环境（例如数据挖掘）中进行决策时，计算机用户将导航至多个步骤，从最初提交查询到评估检索结果，确定结果的可接受程度，然后进入评估状态。互动是否成功。本文将迭代信息搜索（IS）描述为马​​尔可夫过程，在此过程中，用户前进通过“节点”状态。节点是计算机屏幕上的图形对象，代表系统状态以及用户或单个用户对单个节点的信心程度。在检查了节点以建立置信度之后，系统将决策记录为权重，这些权重会影响节点之间转换路径的可能性。通过以这种方式训练系统，该模型被纳入到潜在的马尔可夫过程用户的决策中，以减少不确定性。马尔可夫链成为加权链，IS可以提出合理的建议。

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