基于意见的基于朴素贝叶斯分类器的图书推荐

Anand Shanker Tewari, T. S. Ansari, A. Barman
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引用次数: 10

摘要

在快速发展的电子商务领域,买家被大量的产品信息所包围。然而,像谷歌、百度这样的搜索引擎不能满足购买者的需求,因为用户想要的产品信息不能快速、方便、正确地获得。因此,买家不得不花费大量的时间来删除不必要的信息。许多电子商务网站经常要求买家对他们已经购买的产品进行评论。随着电子商务的日益普及,顾客对产品的评论也越来越多。因此,买家很难阅读所有的评论来做出购买产品的决定。在本文中,我们提取,总结和分类所有的客户评论的一本书。本文提出了一种基于意见挖掘和Naïve贝叶斯分类器的图书推荐技术,向购买者推荐排名靠前的图书。本文还考虑了图书价格等重要的推荐因素,提出了一种新颖的表格式高效的推荐方法,特别适用于首次访问网站的买家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Opinion based book recommendation using Naive Bayes classifier
In the rapidly increasing field of E-commerce, buyer is surrounded by many product information. However, search engines like Google, Baidu, can't satisfy the demands of buyer because the information about the product that the users want can't be obtain quickly, easily and correctly. So buyer has to spend lots of time in removing the unnecessary information. Many e-commerce website often request buyers to review products that they have already purchased. As the popularity of e-commerce is increasing day by day, the reviews from customers about the product receives also increasing heavily. As a result of this it is difficult for a buyer to read all the reviews to make a decision about the product purchase. In this paper, we extracted, summarize and categorize all the customer reviews of a book. This paper proposes a book recommendation technique based on opinion mining and Naïve Bayes classifier to recommend top ranking books to buyers. This paper also considered the important factor like price of the book while recommendation and presented a novel tabular efficient method for recommending books to the buyer, especially when the buyer is coming first time to the website.
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