电子学报 ›› 2016, Vol. 44 ›› Issue (7): 1708-1713.DOI: 10.3969/j.issn.0372-2112.2016.07.027

• 学术论文 • 上一篇    下一篇

基于启发式聚类模型和类别相似度的协同过滤推荐算法

王兴茂, 张兴明, 吴毅涛, 潘俊池   

  1. 国家数字交换系统工程技术研究中心, 河南郑州 450002
  • 收稿日期:2014-12-12 修回日期:2015-10-15 出版日期:2016-07-25 发布日期:2016-07-25
  • 作者简介:王兴茂 男,1989年生于辽宁营口,国家数字交换系统工程技术研究中心硕士生,主要研究方向为数据挖掘、用户行为分析、推荐算法.E-mail:wxmcat@163.com;张兴明 男,1963年生于河南新乡,国家数字交换系统工程技术研究中心教授,主要研究方向为通信与信息系统、宽带信息网络等
  • 基金资助:

    国家973重点基础研究发展计划(No.2012CB315901);国家863高技术研究发展计划(No.2011AA01A103)

A Collaborative Recommendation Algorithm Based on Heuristic Clustering Model and Category Similarity

WANG Xing-mao, ZHANG Xing-ming, WU Yi-tao, PAN Jun-chi   

  1. National Digital Switching System Engineering and Technological R & D Center, Zhengzhou, Henan 450002, China
  • Received:2014-12-12 Revised:2015-10-15 Online:2016-07-25 Published:2016-07-25

摘要:

基于k-近邻的协同过滤推荐算法对于邻居数量k的确定过于主观,并且推荐时以k-近邻均值加权推荐不够准确.针对这两个问题,本文首先引入并改进最大最小距离聚类算法,进而设计启发式聚类模型将用户进行不规定类别数的自由聚类划分,目标用户所在类的用户为邻居用户,客观确定邻居数量;然后在推荐时定义类别相似度,针对性地建立目标用户未评分和评分项目的潜在类别关系,改进k-近邻均值加权算法.实验结果表明,该算法提高了推荐准确度(约0.035MAE).

关键词: 协同过滤, 推荐算法, 聚类算法, 启发式聚类模型, 类别相似度

Abstract:

The collaborative recommendation algorithm based on kNN confirms the number of neighbours subjectively,and is not accurate enough to predict by kNN mean weighting calculating.To address these two problems,the maximum and minimum distance clustering algorithm was introduced and improved to design the heuristic clustering model,the model divided the users allodially without the determination of the category numbers,the neighbours of the target users were the users who were in the same category with the target users;then the category similarity was defined to build the category relation between the unscore and score items of the target user in prediction,and the kNN mean weighting calculating was advanced based on the category similarity.The experiments show that this algorithm improves the degree of accuracy (reducing about 0.035 MAE).

Key words: collaborative, recommendation algorithm, clustering algorithm, heuristic clustering model, category similarity

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