[1]吴月萍 王娜 马良.基于蚁群算法的协同过滤推荐系统的研究[J].计算机技术与发展,2011,(10):73-76.
　WU Yue-ping,WANG Na,MA Liang.Research of Collaboration Filtering Recommendation System Based on Ant Algorithm[J].,2011,(10):73-76.
点击复制

基于蚁群算法的协同过滤推荐系统的研究()
分享到：

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

卷:
期数:
2011年10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:
Research of Collaboration Filtering Recommendation System Based on Ant Algorithm
文章编号:
1673-629X（2011）10-0073-04
作者:
吴月萍1 王娜1 马良2
[1]上海第二工业大学计算机与信息学院[2]上海理工大学管理学院
Author(s):
WU Yue-ping;  WANG Na ; MA Liang
[1]School of Computer and Information, Shanghai Second Polytechnic University[2]College of Management, University of Shanghai for Science and Technology
关键词:
蚁群算法; 聚类; 协同过滤; 推荐; 用户
Keywords:
ant algorithm; clustering;  collaboration filtering;  recommendation;  user
分类号:
TP391
文献标志码:
A
摘要:
协同过滤算法是根据基本用户的观点产生对目标用户的推荐列表，现模拟蚂蚁觅食的原理，将用户视为具有不同属性的蚂蚁，聚类中心视为蚂蚁所要寻找的“食物源”，提出基于蚁群算法实现用户聚类，以提高协同过滤推荐系统的最近邻查询速度，降低搜索开销，同时避免了使用K—Means聚类方法受初始聚类中心和聚类个数的影响。最终实验验证蚁群算法实现用户聚类的有效性，且解决了新用户得不到推荐的问题，并提高了协同过滤推荐算法的精确度
Abstract:
Collaboration filtering recommendation algorithm is that generate the recommendation list according to basic user＇ view. Now imitated ant foraging theory, the users are regarded as different attributes ants, clustering center is regarded as the ＂food source＂ that the ants are looking for, proposed to cluster user based ant algorithm, for improving the query speed of the nearest neighbor in the collabora- tive filtering recommendation system, reducing the search spending, and avoiding the effects of initial clustering centers and clustering numbers in the use of K-Means clustering method. Finally, the experiment verify that user clustering through ant algorithm is effective, and solve the problem of new user not recommended, enhance the precision of collaboration filtering recommendation algorithm

相似文献/References:

[1]段军,张清磊.蚁群算法在LEACH路由协议中的应用[J].计算机技术与发展,2014,24(01):65.
　DUAN Jun,ZHANG Qing-lei.Application of Ant Colony Algorithm Based on LEACH Routing Protocol[J].,2014,24(10):65.
[2]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(10):36.
[3]张甜 罗眉 孟晓红 赵宗涛.一种基于状态特征的航天发射故障诊断技术[J].计算机技术与发展,2010,(01):93.
　ZHANG Tian,LUO Mei,MENG Xiao-hong,et al.A Technology in Fault Diagnosis of Spaceflight Launch Based on State Character[J].,2010,(10):93.
[4]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(10):84.
[5]何小娜 逄焕利.基于二维直方图和改进蚁群聚类的图像分割[J].计算机技术与发展,2010,(03):128.
　HE Xiao-na,PANG Huan-li.Image Segmentation Based on Improved Ant Colony Clustering and Two- Dimensional Histogram[J].,2010,(10):128.
[6]熊伟平 曾碧卿.几种仿生优化算法的比较研究[J].计算机技术与发展,2010,(03):9.
　XIONG Wei-ping,ZENG Bi-qing.Studies on Some Bionic Optimization Algorithms[J].,2010,(10):9.
[7]宋世杰 刘高峰 周忠友 卢小亮.基于改进蚁群算法求解最短路径和TSP问题[J].计算机技术与发展,2010,(04):144.
　SONG Shi-jie,LIU Gao-feng,ZHOU Zhong-you,et al.An Improved Ant Colony Algorithm Solving the Shortest Path and TSP Problem[J].,2010,(10):144.
[8]赵敏 倪志伟 刘斌.K—means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,(04):179.
　ZHAO Min,NI Zhi-wei,LIU Bin.Application Research of K - Means Clustering and Naive Bayesian Algorithm in Business Intelligence[J].,2010,(10):179.
[9]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(10):109.
[10]耿波 仲红 徐杰 闫娜娜.用关联分析法对负荷预测结果进行二次处理[J].计算机技术与发展,2008,(04):171.
　GENG Bo,ZHONG Hong,XU Jie,et al.Using Correlation Analysis to Treat Load Forecasting Results[J].,2008,(10):171.
[11]段凤玲 李龙澍 曹文婷.具有多态特征和聚类处理的蚁群算法[J].计算机技术与发展,2009,(12):77.
　DUAN Feng-ling,LI Long-shu,CAO Wen-ting.Ant Colony Algorithm with Polymorphism and Clustering Processing[J].,2009,(10):77.
[12]刘念涛 刘希玉.基于改进的启发式蚁群算法的聚类问题的研究[J].计算机技术与发展,2007,(08):37.
　LIU Nian-tao,LIU Xi-yu.Research on Clustering Problem Based on Improved Heuristic Ant Colony Algorithm[J].,2007,(10):37.
[13]邵明来,秦亮曦. 集粒度计算、蚁群算法与模糊思想的聚类算法[J].计算机技术与发展,2015,25(02):78.
　SHAO Ming-lai,QIN Liang-xi. Clustering Algorithm Combined Granular Computing,Ant Colony Algorithm and Fuzzy Idea[J].,2015,25(10):78.

备注/Memo

备注/Memo:
国家自然科学基金资助项目（70871081）;上海市重点学科建设资助项目（S30504）吴月萍（1979-），女，江苏常熟人，硕士，工程师，研究方向为数据挖掘、推荐算法；马良，教授，博士，博士生导师，研究方向为算法设计、系统工程
更新日期/Last Update: 1900-01-01