Olivia Foulds, Alessandro Suglia, L. Azzopardi, Martin Halvey
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引用次数: 0
摘要
感知速度(Perceptual Speed, PS)是一种认知能力,它会影响信息检索(Information Retrieval, IR)中的多个因素,如用户的搜索性能和主观体验。然而,PS测试很难管理,这限制了用户自适应系统的设计,这些系统可以自动推断PS,以适当地适应低PS用户。因此,本文使用从TREC Common Core搜索任务日志中提取的特征训练的几个机器学习模型来评估PS是否可以从搜索行为中自动分类。我们的结果是令人鼓舞的:给定用户与一个查询的交互,决策树能够以86%的准确率预测用户的PS为低或高。此外,我们为特定的PS测试确定了不同的行为成分,这意味着每个PS测试测量的是一个人认知能力的不同方面。这些发现激发了人们进一步研究如何更好地设计能够适应个体差异的搜索系统。
Perceptual Speed (PS) is a cognitive ability that is known to affect multiple factors in Information Retrieval (IR) such as a user's search performance and subjective experience. However PS tests are difficult to administer which limits the design of user-adaptive systems that can automatically infer PS to appropriately accommodate low PS users. Consequently, this paper evaluated whether PS can be automatically classified from search behaviour using several machine learning models trained on features extracted from TREC Common Core search task logs. Our results are encouraging: given a user's interactions from one query, a Decision Tree was able to predict a user's PS as low or high with 86% accuracy. Additionally, we identified different behavioural components for specific PS tests, implying that each PS test measures different aspects of a person's cognitive ability. These findings motivate further work for how best to design search systems that can adapt to individual differences.