It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users’ distributions. In this work, we demonstrate that a lack of flatness in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations.

中文翻译：

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更平坦更好

众所周知，推荐系统中的显式用户评分偏向于高评分，并且用户在评分量表的使用方面存在显着差异。实施者通常通过评级归一化或在分解模型中包含用户偏差项来补偿这些问题。然而，这些方法仅针对用户分布的集中趋势进行调整。在这项工作中，我们证明了缺乏平整度评分分布与推荐性能呈负相关。我们提出了一种评级转换模型，该模型通过将评级转换为百分位值作为推荐生成之前的预处理步骤来补偿评级分布中的偏斜及其集中趋势。这种转换使评分分布变平，更好地补偿评分分布的差异，并提高推荐性能。我们还表明，这种转换的平滑版本可以为评分分布非常窄的用户产生更直观的结果。一组全面的实验，在四个真实世界的数据集中使用最先进的推荐算法，显示这些百分位转换的排名性能有所提高。