Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

机译：分析评估舍入模型中的平均绝对误差（MAE）和均方根误差（RMSE）

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Most existing Collaborative Filtering (CF) algorithms predict a rating as the preference of an active user toward a given item, which is always a decimal fraction. Meanwhile, the actual ratings in most data sets are integers. In this paper, we discuss and demonstrate why rounding can bring different influences to these two metrics; prove that rounding is necessary in post-processing of the predicted ratings, eliminate of model prediction bias, improving the accuracy of the prediction. In addition, we also propose two new rounding approaches based on the predicted rating probability distribution, which can be used to round the predicted rating to an optimal integer rating, and get better prediction accuracy compared to the Basic Rounding approach. Extensive experiments on different data sets validate the correctness of our analysis and the effectiveness of our proposed rounding approaches.

机译：大多数现有的协作滤波（CF）算法（CF）算法预测作为活动用户朝向给定项目的偏好的评级，这始终是小数部分。同时，大多数数据集中的实际额定值是整数。在本文中，我们讨论并展示为什么舍入会给这两个指标带来不同的影响;证明在预测评级的后处理中是必要的，消除模型预测偏压，提高预测的准确性，从而提高了舍入。此外，我们还基于预测的评级概率分布提出了两个新的舍入方法，该概率分布可以用于将预测的额定值达到最佳整数额定值，并与基本的舍入方法相比获得更好的预测精度。对不同数据集的广泛实验验证了我们分析的正确性以及我们提出的舍入方法的有效性。

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《International Conference on Mechanical Engineering, Materials Science and Civil Engineering》|2018年|534 p.|共10页
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Weijie Wang; Yanmin Lu;
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中图分类工程材料学;
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Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

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