利用大数据预测亚马逊产品的评级

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2020-12-12 DOI:10.1002/widm.1400

Jongwook Woo, Monika Mishra

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引用次数: 7

摘要

本文旨在将几个机器学习(ML)模型应用于亚马逊电子商务领域的大量数据集，以分析和预测评级并推荐产品。为此，我们使用了传统算法和大数据算法。由于亚马逊产品评论数据量较大，我们提出了适合海量数据存储和计算的大数据架构，这是传统架构无法实现的。此外，该数据集包含15个属性，大约有700万条记录。利用这些数据集，我们在Oracle大数据和Azure云计算服务中开发了几个模型来预测亚马逊上商品的评论评级和推荐。我们对大数据架构Spark ml和传统架构Azure ml的准确率和效率进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting the ratings of Amazon products using Big Data

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.

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.