Personalised Search Time Prediction using Markov Chains
Personalised Search Time Prediction using Markov Chains
复制标题
使用马尔可夫链的个性化搜索时间预测
DOI：
10.1145/3121050.3121085
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
L. Azzopardi
中科院分区：
文献类型：
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作者：
V. Tran;David Maxwell;N. Fuhr;L. Azzopardi
为了提高交互式信息检索（IIR）的效率，系统应该通过适当地引导用户来最小化搜索时间。作为先决条件，在任何搜索情况下，系统必须能够估计用户查找下一个相关文件所需的时间。在本文中，我们将展示如何马尔可夫模型来自搜索日志可以用于预测搜索时间，并描述了一种方法来评估这些预测。为了个性化的预测的基础上观察到的一些用户事件，我们设计适当的参数估计方法。我们的实验结果表明，通过观察用户仅100秒，个性化预测已经明显优于全局预测。
For improving the effectiveness of Interactive Information Retrieval (IIR), a system should minimise the search time by guiding the user appropriately. As a prerequisite, in any search situation, the system must be able to estimate the time the user will need for finding the next relevant document. In this paper, we show how Markov models derived from search logs can be used for predicting search times, and describe a method for evaluating these predictions. For personalising the predictions based upon a few user events observed, we devise appropriate parameter estimation methods. Our experimental results show that by observing users for only 100 seconds, the personalised predictions are already significantly better than global predictions.