Best Approximate Distribution-based Model for Helpful Vote of Customer Review Prediction
Best Approximate Distribution-based Model for Helpful Vote of Customer Review Prediction
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DOI：
10.1109/smc53654.2022.9945190
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ristu Saptono;Tsunenori Mine
中科院分区：
文献类型：
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作者：
Ristu Saptono;Tsunenori Mine
在当今电子商务中，产品评论对于潜在客户的购买决策越来越重要。有帮助的投票是一个关键指标，该审查有多大的影响，对其他客户。因此，预测有用的选票是一项重要的任务。线性回归和Tobit回归是常用的预测方法。这些方法共享相同的目标函数，并且来自于任何数据集上的有用投票遵循正态分布的初始假设。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。因此，预测结果可能不完全合适。本文提出了一种模型，遵循最佳近似分布的有益的选票预测的有益的选票的数量。最重要的是，考虑到自评论撰写以来所经历的时间，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。为了验证所提出的模型，我们在真实世界的数据集上进行了广泛的实验。实验结果验证了该模型的有效性。
Product reviews are more and more important for potential customers to decide on their purchases in electronic commerce nowadays. The helpful vote is a critical indicator of how much impact the review has on other customers. Therefore, the prediction of helpful votes is an essential task. Linear and Tobit Regression are general methods of the prediction. Those methods share the same objective function and come from the initial assumption that the helpful votes on any dataset follow a normal distribution. However, the assumption is not usually confirmed, and the distribution of the helpful votes often follows other distributions. Consequently, the prediction results might not be fully appropriate. This paper proposes a model that follows the best approximate distribution of helpful votes to predict the number of helpful votes. On top of that, considering the elapsed time since reviews were written, we propose an adaptive window size sampling method to evaluate the model on review datasets sorted chronologically. To validate the proposed model, we conducted extensive experiments on real-world datasets. Experimental results illustrate the validity of the proposed model.