Distribution-Adapted Model for Helpful Vote Prediction
Distribution-Adapted Model for Helpful Vote Prediction
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DOI：
10.1109/access.2022.3225558
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Ristu Saptono;Tsunenori Mine
中科院分区：
文献类型：
--
作者：
Ristu Saptono;Tsunenori Mine
评论的有用投票数是衡量评论在电子商务中对其他客户产生多大影响的重要指标。因此，预测有用投票的数量是一项重要的任务。回归分析和Tobit模型是典型的预测方法。这些方法来自相同的初始假设，即有帮助的投票数量在任何数据集上都遵循正态分布。然而，该假设通常不被证实，并且有帮助的选票的分布通常遵循其他分布。本文提出了一个框架，调查建立一个模型，预测有帮助的投票数量的分布根据有帮助的投票的数量的可行性。在此基础上，考虑到评论年龄，我们提出了一种自适应窗口大小采样方法来评估按时间顺序排序的评论数据集上的模型。实验结果表明，采用最佳近似分布的模型较基线模型有明显的改进。此外，使用自适应窗口大小采样方法的模型评估对大数据集的性能有显着影响。
The number of helpful votes on a review is an essential indicator of how much impact the review has on other customers in electronic commerce. Therefore, predicting the number of helpful votes is an important task. Regression analysis and Tobit modeling are typical methods of prediction. Those methods come from the same initial assumption that the number of helpful votes follows a normal distribution on any dataset. However, the assumption is not usually confirmed, and the distribution of the helpful votes often follows other distributions. This paper proposes a framework for investigating the feasibility of building a model that predicts the number of helpful votes according to the distribution of the number of helpful votes. On top of that, considering the review age, we propose an adaptive window size sampling method to evaluate the model on review datasets sorted chronologically. The experimental results validated that the model adapting to the best approximate distribution gives a significant improvement compared to the baseline models. In addition, model evaluation using the adaptive window size sampling method has significant impacts on the performance on large datasets.