[1]张应辉,李雪. 基于模糊聚类的旅游推荐算法[J].计算机技术与发展,2016,26(12):99-102.
　ZHANG Ying-hui,LI Xue. A Tourism Recommendation Algorithm Based on Fuzzy Clustering[J].,2016,26(12):99-102.
点击复制

 基于模糊聚类的旅游推荐算法()
分享到：

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

卷:
26
期数:
2016年12期
页码:
99-102
栏目:
智能、算法、系统工程
出版日期:
2016-12-10

文章信息/Info

Title:
 A Tourism Recommendation Algorithm Based on Fuzzy Clustering
文章编号:
1673-629X（2016)12-0099-05
作者:
 张应辉; 李雪
 东北大学 计算机科学与工程学院
Author(s):
 ZHANG Ying-hui; LI Xue
关键词:
 ; 个性化; 标签; 相似性度量; 模糊聚类; 混合推荐
Keywords:
 ; individualization; tags similarity measurement; fuzzy clustering; hybrid recommendation
分类号:
TP301.6
文献标志码:
A
摘要:
 在旅游领域中，旅游者常常在旅游前从互联网上获取所需信息，但是在线旅游业日益严重的信息过载现象，使得用户不能得到他们想要的个性化信息。传统的基于协同过滤的旅游推荐研究普遍都存在稀疏性和可扩展性等问题，基于知识的推荐研究有时因用户无法表达清楚他们的需求而无法得到满意的推荐。针对已有的旅游推荐算法存在的问题，提出了一种基于模糊聚类的旅游推荐算法，为用户推荐符合其需求和偏好的旅游产品。该算法利用标签构建用户偏好景点模型和景点特征属性模型，对数据集进行模糊聚类，同时提出新的相似度度量。在此基础上，组合基于内容和协同过滤技术进行混合推荐。实验结果表明，该算法能显著提高推荐系统的效率以及可扩展性和准确度。
Abstract:
 In the field of tourism,tourists often get the information they need on the Internet before traveling,but the phenomenon of in-formation overload online in tourism industry is becoming more and more serious,so that personalized information cannot be obtained by users. The problems of sparsity and scalability exist in the traditional tourism recommendation algorithm based on collaborative filtering, and sometimes users can’ t express their needs and can’ t be satisfied with the recommendation based on the knowledge of the recommen-dations. For these problems,a tourism recommendation algorithm based on fuzzy clustering is proposed,which is used for the users to rec-ommend the tourism products that meet their needs and preferences. Tags are used by the algorithm to build user’ s preference models and sights feature attribute model,fuzzy clustering on them. A new similarity measure is proposed. On this basis,the combination of content-based and collaborative filtering technology is recommended. Experimental results show that the proposed algorithm can significantly im-prove the efficiency,scalability and accuracy of the recommendation system.

相似文献/References:

[1]费洪晓 蒋翀 徐丽娟.基于树状向量空间模型的用户兴趣建模[J].计算机技术与发展,2009,(05):79.
　FEI Hong-xiao,JIANG Chong,XU Li-juan.User Profile Based on Dendriform Vector Space Model[J].,2009,(12):79.
[2]王宏超 陈未如 刘俊.基于客户聚类的商品推荐方法的研究[J].计算机技术与发展,2008,(07):212.
　WANG Hong-chao,CHEN Wei-ru,LIU Jun.Research of Commodity Recommendation System Based on Customer Clustering[J].,2008,(12):212.
[3]邱明明 吴国新.一种个性化垃圾邮件识别系统的设计[J].计算机技术与发展,2007,(01):136.
　QIU Ming-ming,WU Guo-xin.Design of a Personal Spam Detection System[J].,2007,(12):136.
[4]姜雅倩 王直杰 张珏.基于供求关系及协同过滤技术的推荐模型研究[J].计算机技术与发展,2007,(06):18.
　JIANG Ya-qian,WANG Zhi-jie,ZHANG Jue.Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering[J].,2007,(12):18.
[5]吴辉娟 袁方.个性化服务技术研究[J].计算机技术与发展,2006,(02):32.
　WU Hui-juan,YUAN Fang.Research of Technologies on Personalized Information Service[J].,2006,(12):32.
[6]申利民 汪新俊.一个自适应Web站点构架的设计[J].计算机技术与发展,2006,(02):157.
　SHEN Li-min,WANG Xin-jun.A Theoretical Framework for Self Adaptive Web Sites[J].,2006,(12):157.
[7]徐科 崔志明.基于搜索历史的用户兴趣模型的研究[J].计算机技术与发展,2006,(05):18.
　XU Ke,CUI Zhi-ming.User Profile Model Based on User Search Histories[J].,2006,(12):18.
[8]石佑红 赵宏 乔敏.Web挖掘在个性化远程教育中的应用[J].计算机技术与发展,2006,(09):136.
　SHI You-hong,ZHAO Hong,QIAO Min.Web Mining Used in Personalized Distance- Learning[J].,2006,(12):136.
[9]丁兆贵 金敏.基于Lucene的个性化搜索引擎研究与实现[J].计算机技术与发展,2011,(02):105.
　DING Zhao-gui,JIN Min.Research and Implementation of Personal Search Engine Based on Lucene[J].,2011,(12):105.
[10]司广涛 孟静 李光顺.基于P2P网络的个性化协同邮件过滤模型[J].计算机技术与发展,2011,(06):128.
　SI Guang-tao,MENG Jing,LI Guang-shun.Personalized Collaborative E-mail Filtering Model Based on P2P Network[J].,2011,(12):128.
[11]胡彬彬,吴绍春. 云服务流定制中的个性化资源推荐方法研究[J].计算机技术与发展,2015,25(03):108.
　HU Bin-bin,WU Shao-chun. Research on Personalized Resource Recommendation Method in Service-flow Customization[J].,2015,25(12):108.
[12]张鸿洋,陈健,翟梅. 基于 Mashup 的个性化移动学习平台[J].计算机技术与发展,2016,26(01):27.
　ZHANG Hong-yang,CHEN Jian,ZHAI Mei. Personalized Mobile Learning Platform Based on Mashup[J].,2016,26(12):27.
[13]王静,祁超,陈健. 基于Mashup技术的PLE模型的研究与实现[J].计算机技术与发展,2016,26(03):53.
　WANG Jing,QI Chao,CHEN Jian. Research and Implementation of PLE Model Based on Mashup Technology[J].,2016,26(12):53.
[14]陈春玲,熊晶,陈琳,等. 加权社会网络中的个性化隐私保护算法[J].计算机技术与发展,2016,26(08):88.
　CHEN Chun-ling,XIONG Jing,CHEN Lin,et al. Personalized Privacy Preservation Algorithm in Weighted Social Networks[J].,2016,26(12):88.

更新日期/Last Update: 2017-02-03