东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (12): 1792-1795.DOI: -

• 论著 • 上一篇    下一篇

基于顾客交易数据的协同过滤推荐方法

赵晓煜;黄小原;曹忠鹏;   

  1. 东北大学工商管理学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-12-15 发布日期:2013-06-22
  • 通讯作者: Zhao, X.-Y.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(70572088)

Improved collaborative filtering recommendation based on customers' transaction data

Zhao, Xiao-Yu (1); Huang, Xiao-Yuan (1); Cao, Zhong-Peng (1)   

  1. (1) School of Business Administration, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-12-15 Published:2013-06-22
  • Contact: Zhao, X.-Y.
  • About author:-
  • Supported by:
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摘要： 分析了B2C电子商务网站中应用最广的协同过滤推荐方法在数据源方面存在的局限性,提出了一种基于顾客交易数据的协同过滤推荐方法.该方法的基本原理是:基于顾客的购买历史记录,获得顾客对于各种商品的最近购买时间R(Recency),购买频率F(Frequency)和购买金额M(Monetary)等指标,利用这三个指标确定顾客对已购商品的偏好程度;进一步建立体现顾客商品偏好度的IRFM矩阵,并以该矩阵为数据源为顾客提供个性化的商品推荐.该方法能为老顾客提供质量更高的推荐,进一步扩展了协同过滤方法的应用范围.

关键词: 推荐系统, 协同过滤, 交易数据, RFM分析, 偏好分析

Abstract: Discussing the limitation of data source in conventional collaborative filtering recommendation (CFR), which is widely used in B2C E-commerce websites, an improved CFR based on customers' transaction data is put forward. The fundamentals of the improved CFR are described as follows. The three customers' behavioral indices including the recency (R), frequency (F), and monetary (M), are acquired from customers' historical records of shopping for various goods to evaluate their purchasing preferences, and then an integrated RFM (IRFM) matrix is formulated as the data source of CFR to recommend personalized goods for target customers, thus providing a high-quality recommendation to familiar customers so as to expand the application range of CFR.

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