Predicting the ratings of Amazon products using Big Data,WIREs Data Mining and Knowledge Discovery

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Predicting the ratings of Amazon products using Big Data
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-12-12 , DOI: 10.1002/widm.1400
Jongwook Woo ₁ , Monika Mishra ₁

Affiliation

This paper aims to apply several machine learning (ML) models to the massive dataset present in the area of e‐commerce from Amazon to analyze and predict ratings and to recommend products. For this purpose, we have used both traditional and Big Data algorithms. As the Amazon product review dataset is large, we present Big Data architecture suitable massive dataset for storing and computation, which is not possible with the traditional architecture. Furthermore, the dataset contains 15 attributes and has about 7 million records. With the dataset, we develop several models in Oracle Big Data and Azure Cloud Computing services to predict the review rating and recommendation for the items at Amazon. We present a comparative conclusion in terms of the accuracy as well as the efficiency with Spark ML—the Big Data architecture, and Azure ML—the traditional architecture.

中文翻译：

使用大数据预测Amazon产品的评分

本文旨在将几种机器学习（ML）模型应用于Amazon电子商务领域中存在的海量数据集，以分析和预测评级并推荐产品。为此，我们同时使用了传统算法和大数据算法。由于亚马逊产品评论数据集很大，因此我们提出了适用于存储和计算的适合海量数据集的大数据架构，这是传统架构无法实现的。此外，数据集包含15个属性，并具有约700万条记录。借助数据集，我们在Oracle大数据和Azure云计算服务中开发了多个模型，以预测亚马逊商品的评论等级和推荐。我们就Spark ML（大数据架构，

更新日期：2020-12-12

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