Domain of interests clustering algorithm based on users’ preferred topics

GONG Wei-hua1; YANG Liang-huai1; JIN Rong2; DING Wei-long1

您当前的位置：

首页 >

文章列表页 >

Domain of interests clustering algorithm based on users’ preferred topics

更新时间：2024-10-14

- Domain of interests clustering algorithm based on users’ preferred topics
- Vol. 32, Issue 1, Pages: 72-78(2011)
- 作者机构：
  
  1. 浙江工业大学计算机学院
  2. 浙江理工大学计算机教研部
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393.01
- Published：2011
- 稿件说明：
移动端阅览
GONG Wei-hua1, YANG Liang-huai1, JIN Rong2, et al. Domain of interests clustering algorithm based on users’ preferred topics[J]. 2011, 32(1): 72-78.
DOI：

GONG Wei-hua1, YANG Liang-huai1, JIN Rong2, et al. Domain of interests clustering algorithm based on users’ preferred topics[J]. 2011, 32(1): 72-78. DOI：

摘要

针对用户兴趣偏好多变问题

提出一种兴趣特征权重随时间而变化的迭代计算方法。构造了用户兴趣特征与主题类间的二部图关系

并在此基础上提出了一种基于主题的用户兴趣聚类算法(TBC)

改变了聚类对象"非此即彼"的硬划分方式。该算法所形成的基于主题的用户兴趣域结构

不仅充分表达了用户的多域兴趣特征和域间主题的联系

还能适应用户兴趣变化。实验表明

TBC算法比传统的K-Means算法以及属于软划分方式的FCM聚类具有更好的用户兴趣划分效果

并且在个性化推荐服务中表现出更高的推荐质量和效率。

Abstract

In order to solve the problem of users’ preferences changing frequently

an iterative computing method was presented to gain the weights of users’ preferences as time goes.A bipartite graph was constructed to show the relations of users’ interests and topic classes.On this base

a novel topic-based clustering（TBC） algorithm was proposed to group the nearest neighbors according to users’ interests

which had changed the usual hard partition method meaning "one or the other" for the clustering items.And the partitioned domains of users’ interests based on multiple topics was also es-tablished by the algorithm

which not only fully profiled users’ interests and the relations of topics indirectly reflected in different domains

but also could adaptively track the changes of users’ interests.Experimental results show that TBC method has better declustering outcome of users’ interests than both the traditional K-Means algorithm and FCM method belonged to the soft clustering

and the TBC algorithm also has higher recommendation quality and better efficiency in personalized recommender services.

关键词

Keywords

references

Views

876

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive clustering federated learning via similarity acceleration

Survey of application of machine learning in wireless channel modeling

Collaborative filtering recommendation algorithm based on rough set rule extraction

Co-pairwise ranking model for item recommendation

Differential privacy protection scheme based on edge betweenness model

Related Author

Binke GU

Guanglu SUN

Suxia ZHU

Jiachi ZHANG

Li YU

Yuanyuan FAN

Jianhua ZHANG

Liu LIU

Related Institution

Heilongjiang Key Laboratory of Intelligent Information Processing and Application

School of Computer Science and Technology, Harbin University of Science and Technology

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications

School of Electronic and Information Engineering, Beijing Jiaotong University

School of computer and information technology,Liaoning Normal University

AI问答

⁰