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首页> 外文会议>ACM conference on recommender systems >Ranking With Non-Random Missing Ratings: Influence Of Popularity And Positivity on Evaluation Metrics
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Ranking With Non-Random Missing Ratings: Influence Of Popularity And Positivity on Evaluation Metrics

机译：具有非随机缺失评分的排名：受欢迎程度和积极性对评估指标的影响

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摘要

The evaluation of recommender systems in terms of ranking has recently gained attention, as it seems to better fit the top-k recommendation task than the usual ratings prediction task. In that context, several authors have proposed to consider missing ratings as some form of negative feedback to compensate for the skewed distribution of observed ratings when users choose the items they rate. In this work, we study two major biases of the selection of items: the first one is that some items obtain more ratings than others (popularity effect), and the second one is that positive ratings are observed more frequently than negative ratings (positivity effect). We present a theoretical analysis and experiments on the Yahoo! dataset with randomly selected items, which show that considering missing data as a form of negative feedback during training may improve performances, but also that it can be misleading when testing, favoring models of popularity more than models of user preferences.
机译：推荐器系统在排名方面的评估最近获得了关注，因为它似乎比通常的评级预测任务更适合前k个推荐任务。在这种情况下，一些作者建议将缺失的评分视为某种形式的负反馈，以补偿用户选择对其评分的项目时观察到的评分的偏斜分布。在这项工作中，我们研究了项目选择的两个主要偏见：第一个是某些项目获得的评分比其他项目更高（人气效应），第二个是正面评价比负面评价更常见（积极效果） ）。我们提供有关Yahoo!的理论分析和实验。具有随机选择项目的数据集，这表明在训练过程中将缺失的数据视为负面反馈的一种形式可能会提高性能，但在测试时可能会产生误导，与用户偏好模型相比，更倾向于普及模型。

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