计算机工程 ›› 2019, Vol. 45 ›› Issue (11): 303-308.doi: 10.19678/j.issn.1000-3428.0052925

• 开发研究与工程应用 • 上一篇    下一篇

基于组合赋权方法的用户购车倾向评价

王茜竹a,b, 韦青霞a,b, 杨晓雅a,b, 康璐璐a,b   

  1. 重庆邮电大学 a. 新一代信息网络与终端协同创新中心;b. 电子信息与网络工程研究院, 重庆 400065
  • 收稿日期:2018-10-18 修回日期:2018-11-21 发布日期:2018-12-11
  • 作者简介:王茜竹(1975-),女,高级工程师、硕士,主研方向为移动物联网通信标准、传输关键技术和移动大数据应用;韦青霞、杨晓雅、康璐璐,硕士研究生。
  • 基金项目:
    教育部-中国移动科研基金(MCM20170203);重庆市自然科学基金(cstc2018jcyjAX0587)。

Evaluation on Car Purchase Tendency for Users Based on Combination Weighting Method

WANG Qianzhua,b, WEI Qingxiaa,b, YANG Xiaoyaa,b, KANG Lulua,b   

  1. a. Collaborative Innovation Center for Information Communication Technology;b. Electronic Information and Networking Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2018-10-18 Revised:2018-11-21 Published:2018-12-11

摘要: 为准确快速地衡量用户的购车倾向,提出一种基于主客观组合赋权的用户购车倾向评价方法。利用多赋权法的兼容性特点保证指标权重的合理性并减少组合权重数量,根据购车倾向与已购车事件的相关性特点,基于真实购车用户数据构造理想点并修正指标权重,以提高购车倾向评价的准确性。实验结果表明,与传统理想点法和未经筛选直接加权的方法相比,该方法可在降低运算复杂度的同时,提高评价效果的准确性。

关键词: 购车倾向评价, 组合赋权, 兼容性, 事件相关性, 理想点广义最小距离

Abstract: In order to accurately evaluate the user's car purchase tendency,an evaluation method on car purchase tendency based on the subjective and objective combination weighting is proposed.The compatibility of multi-weighting method is used to ensure the rationality of index weights and reduce the number of combined weights.According to the correlation characteristics between the car purchase tendency and the purchased car event,the real car user data is used to construct the ideal point,and the index weight is corrected to improve the car purchase tendency evaluation performance.Experimental results show that compared with traditional ideal point method and unscreened direct weighting method,the computational complexity of the method is reduced and the accuracy of the evaluation effect is improved.

Key words: evaluation on car purchase tendency, combination weighting, compatibility, event correlation, generalized minimum distance of ideal point

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