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Popularity Bias in False-positive Metrics for Recommender Systems Evaluation

机译:适用于推荐系统评估的虚假度量的人气偏差

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We investigate the impact of popularity bias in false-positive metrics in the offline evaluation of recommender systems. Unlike their true-positive complements, false-positive metrics reward systems that minimize recommendations disliked by users. Our analysis is, to the best of our knowledge, the first to show that false-positive metrics tend to penalise popular items, the opposite behavior of true-positive metrics-causing a disagreement trend between both types of metrics in the presence of popularity biases. We present a theoretical analysis of the metrics that identifies the reason that the metrics disagree and determines rare situations where the metrics might agree-the key to the situation lies in the relationship between popularity and relevance distributions, in terms of their agreement and steepness two fundamental concepts we formalize. We then examine three well-known datasets using multiple popular true- and false-positive metrics on 16 recommendation algorithms. Specific: datasets are chosen to allow us to estimate both biased and unbiased metric values. The results of the empirical study confirm and illustrate our analytical findings. With the conditions of the disagreement of the two types of metrics established, we then determine under which circumstances true-positive or false-positive metrics should be used by researchers of offline evaluation in recommender systems.
机译:我们调查了推荐系统离线评估中伪证度量对伪证度量的影响。与他们的真正态度不同,虚假的度量标准奖励系统最小化用户不喜欢的建议。我们的分析是,据我们所知,首先表明假冒度量指标倾向于惩罚流行项目,这是真正的度量指标的相反行为 - 导致两种类型的度量在存在偏见的情况下的分歧趋势。我们对指标的理论分析,确定了指标不同意并确定指标可能同意的罕见情况 - 就其协议和陡峭的两个基本而言,情况之间的关键在于流行程度与相关性分布之间的关系我们正规化的概念。然后,我们在16个推荐算法上检查三个众所周知的数据集。特定:选择数据集以允许我们估计偏置和无偏的度量值。实证研究的结果证实并说明了我们的分析结果。随着建立两种类型的指标的分歧的条件,我们确定在哪种情况下,应由推荐系统中的离线评估的研究人员使用真正阳性或假阳性度量。

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