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Mining distinguishing customer focus sets from online customer reviews

机译:挖掘区分客户关注点集和在线客户评论的方法

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摘要

With the development of e-commerce, online shopping becomes increasingly popular. Very often, online shopping customers read reviews written by other customers to compare similar items. However, the number of customer reviews is typically too large to look through in a reasonable amount of time. To extract information that can be used for online shopping decision support, this paper investigates a novel data mining problem of mining distinguishing customer focus sets from customer reviews. We demonstrate that this problem has many applications, and at the same time, is challenging. We present dFocus-Miner, a mining method with various techniques that makes the mined results interpretable and user-friendly. Moreover, we propose a visualization design to display the results of dFocus-Miner. Our experimental results on real world data sets verify the effectiveness and efficiency of our method.
机译:随着电子商务的发展,在线购物变得越来越流行。在线购物客户通常会阅读其他客户撰写的评论,以比较相似的商品。但是,客户评论的数量通常太大,无法在合理的时间内浏览。为了提取可用于在线购物决策支持的信息,本文研究了一种新的数据挖掘问题,即从客户评论中区分出客户关注点集的挖掘。我们证明了这个问题有很多应用,同时也是一个挑战。我们介绍了dFocus-Miner,这是一种采用多种技术的挖掘方法,可使挖掘的结果易于理解且易于使用。此外,我们提出了一种可视化设计来显示dFocus-Miner的结果。我们在真实数据集上的实验结果验证了我们方法的有效性和效率。

著录项

  • 来源
    《Computing》 |2018年第4期|335-351|共17页
  • 作者单位

    School of Computer Science, Sichuan University;

    School of Computer Science, Sichuan University;

    Department of Computer Science and Engineering, Wright State University;

    Faculty of Natural Sciences, University of Tampere;

    School of Computer Science, Sichuan University;

    School of Computer Science, Sichuan University;

    School of Computer Science, Sichuan University;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Distinguishing customer focus; Decision support; Data mining;

    机译:区分客户重点;决策支持;数据挖掘;

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