模型与满意度:更好地理解评价指标

Fan Zhang, Jiaxin Mao, Yiqun Liu, Xiaohui Xie, Weizhi Ma, Min Zhang, Shaoping Ma
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引用次数: 26

摘要

评价指标在红外系统的批量评价中起着重要的作用。基于描述用户如何与排名列表交互的用户模型,定义了一个评估度量,将文档列表的相关性分数与系统有效性和用户满意度的估计联系起来。因此,评估指标的有效性有两个方面:底层用户模型是否能准确预测用户行为,以及评估指标是否与用户满意度相关。虽然已经进行了大量的工作来设计、评估和比较不同的评估指标,但很少有研究探索评估指标的这两个方面之间的一致性。具体来说,我们想要调查的是,是否与用户行为数据校准的指标可以很好地估计用户满意度。为了阐明这一研究问题,我们将各种指标的性能与C/W/L框架进行比较,以估计用户满意度,当它们被优化以适应观察到的用户行为时。在自收集和公共用户搜索行为数据集上的实验结果表明,根据用户点击行为优化的指标与根据用户满意度反馈校准的指标一样出色。我们还研究了评估指标校准过程中的可靠性,以找出参数调整需要多少数据。我们的研究结果为用户行为建模与满意度测量之间的一致性提供了实证支持,并为评估指标参数的调整提供了指导。
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Models Versus Satisfaction: Towards a Better Understanding of Evaluation Metrics
Evaluation metrics play an important role in the batch evaluation of IR systems. Based on a user model that describes how users interact with the rank list, an evaluation metric is defined to link the relevance scores of a list of documents to an estimation of system effectiveness and user satisfaction. Therefore, the validity of an evaluation metric has two facets: whether the underlying user model can accurately predict user behavior and whether the evaluation metric correlates well with user satisfaction. While a tremendous amount of work has been undertaken to design, evaluate, and compare different evaluation metrics, few studies have explored the consistency between these two facets of evaluation metrics. Specifically, we want to investigate whether the metrics that are well calibrated with user behavior data can perform as well in estimating user satisfaction. To shed light on this research question, we compare the performance of various metrics with the C/W/L Framework in estimating user satisfaction when they are optimized to fit observed user behavior. Experimental results on both self-collected and public available user search behavior datasets show that the metrics optimized to fit users' click behavior can perform as well as those calibrated with user satisfaction feedback. We also investigate the reliability in the calibration process of evaluation metrics to find out how much data is required for parameter tuning. Our findings provide empirical support for the consistency between user behavior modeling and satisfaction measurement, as well as guidance for tuning the parameters in evaluation metrics.
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