一种基于RFM模型的新型协同过滤个性化推荐算法

张宁; 范崇睿; 张岩

doi:10.11959/j.issn.1000-0801.2015180

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一种基于RFM模型的新型协同过滤个性化推荐算法

研究与开发 | 更新时间：2024-06-05

- 一种基于RFM模型的新型协同过滤个性化推荐算法
- A Novel Personalized Recommendation Algorithm of Collaborative Filtering Based on RFM Model
- 电信科学 2015年31卷第9期页码：103-111
- 作者机构：
  
  1. 北京石油化工学院北京102617
  2. 北京航空航夭大学北京102206
  3. 北京化工大学北京100029
- 作者简介：
  
  [ "张宁，女，博士，北京石油化工学院教授、硕士生导师，主要研究方向为计算机通信网络、控制理论与控制工程。" ]
  [ "范崇睿，男，北京航空航天大学在读，主要研究方向为控制科学、电气工程。" ]
  [ "张岩，男，北京化工大学硕士生，主要研究方向为推荐算法、系统软件。" ]
- 基金信息：
  
  北京市大学生研究训练项目;Research and Training Program for College Students in Beijing(14010321029)
- DOI：10.11959/j.issn.1000-0801.2015180
  中图分类号：
- 网络出版日期：2015-09，
  
  纸质出版日期：2015-09-20
- 稿件说明：
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张宁, 范崇睿, 张岩. 一种基于RFM模型的新型协同过滤个性化推荐算法[J]. 电信科学, 2015,31(9):103-111.

Ning Zhang, Chongrui Fan, Yan Zhang. A Novel Personalized Recommendation Algorithm of Collaborative Filtering Based on RFM Model[J]. Telecommunications science, 2015, 31(9): 103-111.
张宁, 范崇睿, 张岩. 一种基于RFM模型的新型协同过滤个性化推荐算法[J]. 电信科学, 2015,31(9):103-111. DOI： 10.11959/j.issn.1000-0801.2015180.

Ning Zhang, Chongrui Fan, Yan Zhang. A Novel Personalized Recommendation Algorithm of Collaborative Filtering Based on RFM Model[J]. Telecommunications science, 2015, 31(9): 103-111. DOI： 10.11959/j.issn.1000-0801.2015180.

摘要

摘要：为了提高个性化推荐效果及预测准确度，特别是针对传统算法中评分矩阵过于稀疏等问题提出一种新颖的协同过滤算法。该算法首先利用RFM模型合理地筛选用户信息，其次通过黏性客户的消费记录稠密化用户—项目评分矩阵，并改进了传统相似度计算公式。通过仿真实验证实了算法的准确性，最后将其应用于一套具有个性化商品推荐功能的系统原型中，证明了该推荐算法的有效性及实用性。

Abstract

In order to improve the accuracy of recommendation

especially the matrix score of personalized recommendation technology is too spars

a new recommendation algorithm was proposed. The advantages of this algorithm were mainly embodied in the following aspects. Firstly

the improved algorithm with RFM model was used to select the original customer in some condition

making the recommended source of data more accurate and efficient. Secondly

in the improved algorithm the customer consumption history records were filled to the matrix to improve the consistency of the matrix of score. Thirdly

the traditional Pearson similarity calculation formula was improved to make the search of target users of similar neighbor more accurate. Then the simulation experiment was carried on by using the improved algorithm. It can be proved that the improved algorithm is better than the traditional one in accuracy. At last

the improved algorithm was applied to a recommendation system with personalized recommendation function. It was shown that the recommendation algorithm was efficient and valid.

关键词

Keywords

references

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