[1]徐红 彭黎 郭艾寅 徐云剑.基于用户多兴趣的协同过滤策略改进研究[J].计算机技术与发展,2011,(04):73-76.
　XU Hong,PENG Li,GUO Ai-yin,et al.User-Based Collaborative Filtering Strategies More Interested in Improvement of Research[J].,2011,(04):73-76.
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基于用户多兴趣的协同过滤策略改进研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年04期
页码:
73-76
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
User-Based Collaborative Filtering Strategies More Interested in Improvement of Research
文章编号:
1673-629X（2011）04-0073-04
作者:
徐红1; 2 彭黎1 郭艾寅2 徐云剑2
[1]湖南大学软件学院[2]湖南涉外经济学院计算机科学与技术学部
Author(s):
XU Hong; PENG Li; GUO Ai-yin; XU Yun-jian
[1]Software School of Hunan University[2]Department of Computer Science and Technoloty,Hunan International Economics University
关键词:
协同过滤; 基于时间的数据阈值; 基于兴趣的数据权重; 用户多兴趣的表示
Keywords:
collaborative filtering;  time threshold;  weights based on the data of interest;  users expressed more interest
分类号:
TP31
文献标志码:
A
摘要:
协同过滤机制利用用户之间的相似性来推荐信息,能够为用户发现新的感兴趣的内容,它作为一种行之有效的技术被应用到很多领域中。但传统的协同过滤算法不能反映用户的多个兴趣及兴趣更新情况。基于此不足,在用户聚类协同过滤算法的基础上进行了改进,其基本思想是在基于用户聚类的基础上研究用户多兴趣的表示。针对用户兴趣偏好及更新情况引入基于时间的数据阈值、兴趣类型和用户项目兴趣权重的概念和公式。在此基础上将它们有机结合,融入基于用户聚类的协同过滤算法的推荐过程中。实验表明,改进后的算法比传统协同过滤算法在推荐准确度上有明显提高
Abstract:
Collaborative filtering using the similarity between users to recommend information to users interested in discovering new content,it acts as an effective technology has been applied to many fields.However,the traditional collaborative filtering algorithms can not reflect the multi-user interest and user interest changes.To address this problem,a collaborative filtering based on user clustering strategies to improve the basic idea is the basis of user-based clustering of users and more interested in that.Time threshold and the introduction of user interest data on the weight of the concepts,definitions and formulas to calculate the user preferences for different project categories and changes of interest.On this basis,they will combine the introduction of user-based collaborative filtering algorithm for clustering the recommended process.Experimental results show that the improved algorithm than the traditional collaborative filtering recommendation algorithm accuracy are dramatically increased

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备注/Memo

备注/Memo:
湖南省教育科学研究项目（09C591 09C600）徐红（1976-），女，湖南长沙人，硕士，讲师，研究方向为数据挖掘和个性化服务；彭黎，博士，副教授，研究方向为数据挖掘、系统分析与建模、软件工程
更新日期/Last Update: 1900-01-01