[1]方超,暴建民,薛四猛. 基于领域特征值的协同过滤个性化推荐方法[J].计算机技术与发展,2017,27(11):88-91.
　FANG Chao,BAO Jian-min,XUE Si-meng. A Personalized Collaborative Filtering Recommendation Method Based on Domain Features[J].,2017,27(11):88-91.
点击复制

 基于领域特征值的协同过滤个性化推荐方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
88-91
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 A Personalized Collaborative Filtering Recommendation Method Based on Domain Features
文章编号:
1673-629X（2017)11-0088-04
作者:
 方超; 暴建民; 薛四猛
 南京邮电大学 物联网学院
Author(s):
 FANG Chao; BAO Jian-min; XUE Si-meng
关键词:
 ; 领域特征值; 协同过滤; 用户偏好模型; 个性化推荐
Keywords:
 ; domain features; collaborative filtering; user preference model; personalized recommendation
分类号:
TP301
文献标志码:
A
摘要:
 知识发现领域中,个性化推荐技术因其应用广泛受到了业界的广泛关注和高度重视.但由于用户隐私保护方面的限制,现有的推荐系统不能直接挖掘用户的个人信息,因此只能采用表征用户爱好的特征值来间接地挖掘用户信息.针对此类问题,提出了一种新的推荐方法.该方法可自动提取相应领域的特征值,并基于领域关键词过滤冗余的领域特征值,从而据此构建用户偏好模型,并与协同过滤算法绑定,生成最终的推荐结果.为验证所提出推荐方法的有效性和可行性,基于实时数据集与其他已有的推荐方法进行了对比实验,并基于对比实验结果进行了相关的分析研究.对比验证实验结果及其分析表明,该推荐方法能够有效地提取领域特征值,其推荐的精准度也有所提高.
Abstract:
 In knowledge discovery,personalized recommendation technology has received extensive concern and high attention because of its wide application. However,due to the limitations of user privacy protection,the existing recommendation system can’ t directly mine the user’ s personal information. So,the features which imply user preference to indirectly mine user information can be utilized. In order to solve above problem,a new recommendation method is proposed which can automatically extract relevant domain features and filter the redundant domain features based on domain keywords to construct a user preference model and generate the final recommendation result in combination with the collaborative filtering algorithm. To verify its effectiveness and feasibility,compared with other existing recom-mendation methods based on a real time data sets the experiments for verification are conducted. The results of contrast experiments and relevant analysis show that it can effectively extract the domain features and its accuracy of the recommendation is improved.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(11):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(11):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(11):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(11):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(11):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(11):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(11):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(11):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(11):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(11):47.

更新日期/Last Update: 2017-12-26