Please wait a minute...
Advanced Search
数据分析与知识发现  2017, Vol. 1 Issue (9): 28-39     https://doi.org/10.11925/infotech.2096-3467.2017.09.03
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于标签的商品推荐模型研究*
涂海丽1(), 唐晓波2
1东华理工大学经济与管理学院 南昌 330013
2武汉大学信息管理学院 武汉 430072
Building Product Recommendation Model Based on Tags
Tu Haili1(), Tang Xiaobo2
1School of Economics and Management, East China University of Technology, Nanchang 330013, China
2School of Information Management, Wuhan University, Wuhan 430072, China
全文: PDF (1110 KB)   HTML ( 2
输出: BibTeX | EndNote (RIS)      
摘要 

目的】构建社会化电子商务环境下基于标签的个性化商品推荐模型。【方法】综合考虑用户使用标签的频率和时间因素计算用户的兴趣偏好; 基于标签层次特征和电子商务网站中关于商品特征的检索条件, 构建某一主题商务社区中商品本体; 利用本体规范化用户标签语义, 并对商品进行分类; 寻找含有用户偏好的类簇, 计算该类簇中商品与用户偏好商品的相似度, 将用户未标注过的商品与用户偏好相似度高的商品推荐给用户。【结果】从翻东西网站上随机选取200个活跃用户关于热门商品的标注信息进行分析, 验证该模型的有效性。【局限】在计算用户兴趣偏好时, 只考虑用户使用标签的频率和时间因素, 未考虑其他因素。【结论】该模型相对于利用标签进行协同过滤推荐方法具有较优的效果, 计算时间和空间复杂度更小。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
涂海丽
唐晓波
关键词 用户标签商品本体用户偏好推荐模型    
Abstract

[Objective] This paper proposes a personalized product recommendation model based on tags in the social e-commerce environment. [Methods] First, we calculated users’ interests and preferences with the help of tagging frequency and time. Then, we constructed a product ontology of the commercial community based on the tag features and searching conditions of the e-commerce website. Third, we used the ontology to standardize tag semantics, and to classify goods. Fourth, we found clusters containing user preferences, and calculated the similarity between their tags of goods and user preference in the cluster. Finally, we identified the goods which were not tagged but preferred by a specific user. [Results] We examined the model with information of 200 randomly selected active users of popular items from the website of FanDongXi. [Limitations] Only used the frequency and time factor of the users’ tags to calculate their interests and preferences. [Conclusions] The proposed method has better performance than the collaborative filtering recommendation based methods.

Key wordsUser Tag    Product Ontology    User Preference    Recommendation Model
收稿日期: 2016-12-07      出版日期: 2017-10-18
ZTFLH:  G35  
基金资助:*本文系国家自然科学基金项目“社会化媒体集成检索与语义分析方法研究”(项目编号: 71273194)、抚州市社科规划项目“基于KANO模型的抚州旅游市场需求分析”(项目编号: 15sk23)和东华理工大学地质资源经济与管理研究中心开放基金项目“基于KANO模型的旅游用户潜在需求挖掘研究”(项目编号: 14GL05)的研究成果之一
引用本文:   
涂海丽, 唐晓波. 基于标签的商品推荐模型研究*[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
Tu Haili,Tang Xiaobo. Building Product Recommendation Model Based on Tags. Data Analysis and Knowledge Discovery, 2017, 1(9): 28-39.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.09.03      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I9/28
  基于标签的商品推荐模型
  社会化电子商务中的用户-标签-商品关系示例
  标签层次及对应结构
  基于标签的本体构建
  用户标签权重计算结果(部分)
  不同K值下的Precision值比较
  不同K值下的Recall值比较
  不同K值下的F-Measure
[1] 于洪, 李俊华. 结合社交与标签信息的协同过滤推荐算法[J]. 小型微型计算机系统, 2013, 34(11): 2467-2471.
doi: 10.3969/j.issn.1000-1220.2013.11.010
[1] (Yu Hong, Li Junhua.Collaborative Filtering Recommendation Algorithm Using Social and Tag Information[J]. Journal of Chinese Computer Systems, 2013, 34(11): 2467-2471.)
doi: 10.3969/j.issn.1000-1220.2013.11.010
[2] Ji A T, Yeon C, Kim H, et al.Collaborative Tagging in Recommender Systems[C]// Proceedings of the 20th Australian Joint Conference on Artificial Intelligence. Berlin: Springer-Verlag, 2007: 377-386.
[3] Marinho L B, Schmidt-Thieme L. Collaborative Tag Recommendations [EB/OL]. [2016-05-25]. .
[4] Nakamoto R, Nakajima S, Miyazaki J, et al.Tag-based Contextual Collaborative Filtering[J]. IAENG International Journal of Computer Science, 2007, 34(2): 214-219.
[5] Zhao S, Du N, Nauerz A, et al.Improved Recommendation Based on Collaborative Tagging Behaviors[C]//Proceedings of the International Conference on Intelligent User Interfaces. New Mexico: ACM Press, 2008: 413-416.
[6] Gu Y, Yang Z, Kitsuregawa M. Towards Effective Recommendation in Asocial Annotation System Through Group Extraction [EB/OL]. [2011-12-01]. .
[7] Niwa S, Doi T, Honiden S.Web Page Recommender System Based on Folksonomy Mining[C]//Proceedings of the 3rd International Conference on Information Technology. 2006: 388-393.
[8] Gemmell J, Shepitsen A, Mobasher B, et al.Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering [A]// Data Warehousing and Knowledge Discovery[M]. Springer, Berlin, Heidelberg, 2008: 196-205.
[9] 杨丹, 曹俊. 基于Web2.0的社会性标签推荐系统[J].重庆工学院学报: 自然科学版, 2008, 22(7): 52-53.
doi: 10.3969/j.issn.1674-8425-B.2008.07.013
[9] (Yang Dan, Cao Jun.Web Page Recommender System Based on Social Tags in Web 2.0[J]. Journal of Chongqing Institute of Technology, 2008, 22(7): 52-53.)
doi: 10.3969/j.issn.1674-8425-B.2008.07.013
[10] Zhang Z, Zhou T, Zhang Y.Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(1): 179-186.
doi: 10.1016/j.physa.2009.08.036
[11] Li D, Xu Z, Yang M, et al.Item Recommendation in Social Tagging Systems Using Tag Network[J]. Journal of Information and Computational Science, 2013, 10(13): 4057-4066.
doi: 10.12733/jics20102056
[12] Hotho A, Jäschke R, Schmitz C, et al.FolkRank: A Ranking Algorithm for Folksonomies[C]// Proceedings of the 2006 Lernen-Wissensentdeckung-Adaptivität (LWA 2006). 2006: 111-114.
[13] Schmitz C, Hotho A, Jäschke R, et al.Mining Association Rules in Folksonomies[A]// Data Science and Classification[M]. Berlin: Springer-Verlag, 2006: 261-270.
[14] 曹高辉, 毛进. 基于协同标注的B2C电子商务个性化推荐系统研究[J]. 图书情报工作, 2008, 52(12): 126-128.
[14] (Cao Gaohui, Mao Jin.Research on a Collaborative Tagging System for Personalized Recommendation in B2C Electronic Commerce[J]. Library and Information Service, 2008, 52(12): 126-128.)
[15] 田莹颖. 基于社会化标签系统的个性化信息推荐探讨[J]. 图书情报工作, 2010, 54(1): 50-54.
[15] (Tian Yingying.On Personalized Information Recommendation Based on Social Tagging System[J]. Library and Information Service, 2010, 54(1): 50-54.)
[16] 邓双义. 基于语义的标签推荐系统关键问题研究[D].上海: 华东师范大学, 2009.
[16] (Deng Shuangyi.Research on Key Problems of Tag Recommendation System Based on Semantic [D]. Shanghai: East China Normal University, 2009.)
[17] Rafailidis D, Daras P.The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: System, 2013, 43(3): 673-688.
doi: 10.1109/TSMCA.2012.2208186
[18] 郭娣, 赵海燕. 融合标签流行度和时间权重的矩阵分解推荐算法[J]. 小型微型计算机系统, 2016, 37(2): 293-297.
[18] (Guo Di, Zhao Haiyan.Matrix Factorization Recommendation Algorithm Fusing Tag Popularity and Time Weight[J]. Journal of Chinese Computer Systems, 2016, 37(2): 293-297.)
[19] Durão F A, Dolog P.Analysis of Tag-Based Recommendation Performance for a Semantic Wiki[C]// Proceedings of the 6th European Semantic Web Conference, Hersonissos, Greece. 2009.
[20] Cheng Y, Qiu G, Bu J J, et al.Model Bloggers’ Interests Based on Forgetting Mechanism[C]//Proceedings of the 17th International Conference on World Wide Web. New York: ACM Press, 2008: 1129-1130.
[21] 蔡强, 韩东梅, 李海生, 等. 基于标签和协同过滤的个性化资源推荐[J]. 计算机科学, 2014, 41(1): 69-71, 110.
[21] (Cai Qiang, Han Dongmei, Li Haisheng, et al.Personalized Resource Recommendation Based on Tags and Collaborative Filtering[J]. Computer Science, 2014, 41(1): 69-71, 110.)
[22] 赵艳, 王亚民. P2P环境下基于社会化标签的个性化推荐模型研究[J]. 现代图书情报技术, 2014(5): 50-57.
[22] (Zhao Yan, Wang Yamin.Model for Personalized Recommendation Based on Social Tagging in P2P Environment[J]. New Technology of Library and Information Service, 2014(5): 50-57.)
[1] 苏庆,陈思兆,吴伟民,李小妹,黄佃宽. 基于学习情况协同过滤算法的个性化学习推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[2] 陆伟,罗梦奇,丁恒,李信. 深度学习图像标注与用户标注比较研究*[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[3] 侯银秀, 李伟卿, 王伟军, 张婷婷. 基于用户偏好与商品属性情感匹配的图书个性化推荐研究*[J]. 数据分析与知识发现, 2017, 1(8): 9-17.
[4] 朱玲,薛春香,章成志,傅柱. 微博用户标签与博文内容相关度研究*[J]. 现代图书情报技术, 2016, 32(3): 18-24.
[5] 王伟军, 宋梅青. 一种面向用户偏好定向挖掘的协同过滤个性化推荐算法[J]. 现代图书情报技术, 2014, 30(6): 25-32.
[6] 朱恒民, 贾丹华, 黄震奇, 王春晖. 互联网用户偏好本体实例的学习方法研究[J]. 现代图书情报技术, 2013, 29(7/8): 43-48.
[7] 滕广青, 毕达天, 任晶, 陈晓美. Folksonomy中用户标签的语义紧密性研究[J]. 现代图书情报技术, 2013, (12): 48-54.
[8] 汪英姿. 基于本体的个性化图书推荐方法研究[J]. 现代图书情报技术, 2012, (12): 72-78.
[9] 黄红霞, 章成志. 中文微博用户标签的调查分析——以新浪微博为例[J]. 现代图书情报技术, 2012, (10): 49-54.
[10] 张云中, 杨萌, 徐宝祥. 基于FCA的Folksonomy用户偏好挖掘研究[J]. 现代图书情报技术, 2011, 27(6): 72-78.
[11] 路永和, 曹利朝. 基于关联规则综合评价的图书推荐模型[J]. 现代图书情报技术, 2011, 27(2): 81-86.
[12] 赵妍, 苏玉召, 管涛. 一种提高过滤用户偏好精度的数据采集方法[J]. 现代图书情报技术, 2011, (11): 31-37.
[13] 王翠英. 基于Folksonomies的用户偏好挖掘研究[J]. 现代图书情报技术, 2009, 25(6): 37-43.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn