基于关联规则的客户行为建模与商品推荐研究

林穗; 郑志豪

doi:10.12052/gdutxb.180015

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基于关联规则的客户行为建模与商品推荐研究

综合研究 | 更新时间：2026-05-27

- 基于关联规则的客户行为建模与商品推荐研究
- A Research of a Recommender System Based on Customer Behavior Modeling by Mining Association Rules
- 广东工业大学学报 2018年35卷第3期页码：90-94
- 作者机构：
  
  广东工业大学　计算机学院,广东,广州,510006
- 作者简介：
  
  林穗(1972–)，女，副教授，主要研究方向为云计算、云存储、操作系统、数据挖掘等
  郑志豪(1992–)，男，硕士研究生，主要研究方向为推荐系统、数据挖掘等. E-mail：694473924@qq.com
- 基金信息：
  
  广州市科技计划项目(2017010160012)
- DOI：10.12052/gdutxb.180015
  中图分类号： TP311
- 纸质出版：2018
- 稿件说明：
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林穗, 郑志豪. 基于关联规则的客户行为建模与商品推荐研究[J]. 广东工业大学学报, 2018, 35(3): 90-94.

Lin Sui, Zheng Zhi-hao. A Research of a Recommender System Based on Customer Behavior Modeling by Mining Association Rules[J]. 2018, 35(3): 90-94.
林穗, 郑志豪. 基于关联规则的客户行为建模与商品推荐研究[J]. 广东工业大学学报, 2018, 35(3): 90-94. DOI： 10.12052/gdutxb.180015.

Lin Sui, Zheng Zhi-hao. A Research of a Recommender System Based on Customer Behavior Modeling by Mining Association Rules[J]. 2018, 35(3): 90-94. DOI： 10.12052/gdutxb.180015.

摘要

随着我国电子商务事业的发展

传统的电子商务服务模式已经不能满足人们的购物需求

针对客户个性化推荐的研究具有一定的意义. 本文将Apriori算法进行改进

利用改进的Apriori算法对用户兴趣信息进行挖掘

挖掘用户之间的关联性

建立用户行为模型

为用户推荐其感兴趣的商品

提升用户的购买体验. 实验表明

改进的算法提高了推荐的精度和速度.

Abstract

With the development of e-commerce in China

the traditional e-business service mode can no longer meet people’s shopping needs. Personalized recommendation for customers is a problem worthy of study. In this research

the improvement of Apriori algorithm is used to mine user interest information and the user correlation. Then

a user behavior model is set up

and can recommend the goods of interest

and improve the user’s purchase experience. Experiments show that the improved Apriori algorithm improves the accuracy and speed of the recommendation system.

关键词

Keywords

references

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