东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (1): 138-143.DOI: 10.12068/j.issn.1005-3026.2019.01.026

• 管理科学 • 上一篇    下一篇

基于多属性在线评价信息的商品购买推荐排序方法

张瑾， 尤天慧， 樊治平   

  1. (东北大学 工商管理学院， 辽宁 沈阳110169)
  • 收稿日期:2017-10-20 修回日期:2017-10-20 出版日期:2019-01-15 发布日期:2019-01-28
  • 通讯作者: 张瑾
  • 作者简介:张瑾(1989-)，女，山东莒县人，东北大学博士研究生； 尤天慧(1967-)，女，黑龙江宾县人，东北大学教授，博士生导师； 樊治平(1961-)，男，江苏镇江人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(71571039).

Method for Product Purchasing Recommendation Ranking Based on Multi-attribute Online Ratings Information

ZHANG Jin， YOU Tian-hui， FAN Zhi-ping   

  1. School of Business Administration， Northeastern University， Shenyang 110169， China.
  • Received:2017-10-20 Revised:2017-10-20 Online:2019-01-15 Published:2019-01-28
  • Contact: YOU Tian-hui
  • About author:-
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摘要： 针对支持消费者购买决策，提出了一种基于多属性在线评价信息的商品购买推荐排序方法.在该方法中，首先将消费者关注的备选商品各属性在线评价信息转化为关于属性评价标度的概率分布，并确定备选商品各属性在线评价结果的累积分布函数，进而构建加权累积分布函数决策矩阵;然后，依据该决策矩阵，确定正、负理想商品加权累积分布向量，并计算各备选商品与正、负理想商品的加权累积分布向量的距离以及相应的贴近度;进一步地，依据贴近度的大小，可确定备选商品的推荐排序结果.最后，以一个支持消费者购买轿车决策为例说明了该方法的可行性和有效性.

关键词: 商品购买决策, 在线评价信息, 概率分布, TOPSIS, 推荐排序

Abstract: In order to support consumers’ purchasing decision， a product purchasing recommendation ranking method based on multi-attribute online ratings information was proposed. First， the online rating information on each product attribute was transformed into the probability distributions of attribute rating scales， and the cumulative distribution functions of online evaluation results to each product attribute were obtained. Then， the weighted cumulative distribution functions decision matrix was constructed， and the weighted cumulative distribution vectors of the ideal and anti-ideal products were determined based on the decision matrix. Furthermore， the distances between the weighted cumulative distribution vectors of each alternative product and the ideal/anti-ideal product were calculated， and the closeness coefficient of each alternative product was obtained. According to the closeness coefficient， the recommendation ranking results of alternative products were determined. Finally， an example to support consumers’ car purchasing decision was used to illustrate the feasibility and validity of the proposed method.

Key words: product purchasing decision, online ratings information, probabilistic distribution, TOPSIS(technique for order preference by similarity to an ideal solution), recommendation ranking

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