期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

足球视频搜索引擎中的用户偏好挖掘 被引量：2

User preference mining for soccer video search engine
原文传递
导出
摘要 目的互联网信息量的急速增长使得人们需要花费大量时间从搜索引擎召回的结果中浏览自身感兴趣的内容,结合用户的搜索日志信息和社交平台信息,提出一种分层的实时偏好挖掘模型,为用户提供个性化搜索服务。方法在系统分析偏好挖掘的国内外研究现状的基础上,针对足球视频,提出一种分层权重无向图(HWUG)用户偏好模型,充分考虑用户偏好之间的关联信息,通过获取用户在足球领域的显式和隐式反馈信息,提取反馈信息中的偏好标签和偏好动作,并引入时间衰减因子,实现用户足球偏好的实时计算。结果算法已经应用在搜球网(www.findball.net)的个性化检索结果排序和视频推荐上,并已经取得了很好的效果。结论实验结果表明,结合特定领域的知识,基于分层无向权重图模型的偏好挖掘算法能更准确和实时反映用户的足球偏好。 Objective With the explosive growth of internet information, although search engines can provide convenient in- formation retrieval services, people still have to spend a lot of time and energy to find the information they are interested in from millions of search results. Therefore, it is significant to mine user＇s preference from the search engine＇s interactive in- formation and to provide personalized search service. Method Our method is based on a systematic analysis of current re- searches on user preference＇ mining. We propose a novel preference model named HWUG, which fully considers the rela- tionship between the user preference information. It can extract preference label and action from the user＇s explicit and im- plicit feedback information. A time attenuation factor is brought into the historic preference information in order to provide the real-time computation of preference information. Result The proposed methods have been used in the soccer video search engine （www. findballl, net ） and achieved good results in personalized video retrieval and recommendation. Conclusion The experimental results show that the combination of domain-specific knowledge, based on a hierarchical graph model undirected weighted preferece mining algorithm can more accurately reflect the user＇s soccer preferences in real-time.
作者 胡雨成 于俊清 黄贤强 何云峰 管涛
机构地区 华中科技大学计算机科学与技术学院 华中科技大学网络与计算中心
出处 《中国图象图形学报》 CSCD 北大核心 2014年第4期622-629,共8页 Journal of Image and Graphics
基金 国家自然科学基金项目(61173114 61202300) 湖北省杰出青年基金项目(2010CDA084) 广东省产学研项目(2011B090400251) 中央高校基本科研业务费专项资金项目(2011QN057 2011TS094)
关键词 互联网 足球视频 搜索引擎 用户反馈 偏好挖掘 视频推荐 Internet soccer video search engine user feedback preference mining video recommendation
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献15

  • 1Sanasam R S, Murthy H A, Gonsalves T A. Determining user~ interest in real time [ C ]//Proceedings of the 17th International Conference on World Wide Web. New York, USA : ACM, 2008 : 1115-1116. [ DOI : 10.1145/1367497. 1367683 ].
  • 2Chen W Y, Zhang D, Chang E Y. Combinational collaborative filtering for personalized community recommendation [ C ]//Pro- ceedings of the 14th ACM SIGKDD internationa conference on Knowledge discovery and data mining. New York, USA: ACM, 2008 : 115-123. [DOI : 10.1145/1401890. 1401909 ].
  • 3Sun T, Wang L, Guo Q. A collaborative filtering recommenda- tion algorithm based on item similarity of user preference [ C ]// Proceedings of Second International Workshop on Knowledge Dis- covery and Data Mining. Moscow: IEEE, 2009 : 60-63. [DOI: 10.1109/WKDD. 2009.90.
  • 4Xu G, Yang S H, Li H. Named entity mining from click-through data using weakly supervised latent diriehlet allocation [ C ]//Pro- ceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2009 : 1365-1374. [DOI: 10.1145/1557019. 1557165 ].
  • 5Li M, Zheng Y, Neo S Y, et al. Personalized event-based news video retrieval with dynamic user-log[ C ]//Proceedings of IEEE International Conference on Multimedia and Expo. Hannover: IEEE, 2008: 1157-1160. [DOI: 10. ll09/ICME. 2008. 4607645 ].
  • 6Zhang Y, Zhang X, Xu C, et al. Personalized retrieval of sports video[ C ]//Proceedings of the International Workshop on Multi- media Information Retrieval. New York, USA : ACM, 2007 : 313-322. [DOI : 10.1145/1290082. 1290126 ].
  • 7Zhang L, Zhang Y. Interactive retrieval based on faceted feed- back[ C]//Proceedings of the 33rd International ACM SIGIR Conference on Research and Developmet in Information Retriev- al. New York, USA: ACM, 2010: 363-370. [DOI: 10. 1145/ 1835449. 1835511 ].
  • 8Park J, Lee S J, Lee S J, et al. Online video recommendation through tag-cloud aggregation [ J ]. IEEE MultiMedia, 2011, 18 ( 1 ) : 78-86. [ DOI: 10.1109/MMUL. 2010.6 ].
  • 9Li H, Murata T. Improving personalized recommendation in VSS based on user's preference [ C ]//Proceedings of the International MultiConferenee of Engineers and Computer Scientists. Hang Kong, China: Newswood Limited Press, 2012:580-584.
  • 10Chen B, Wang J, Huang Q, et al. Personalized video recommen- dation through tripartite graph propagation [ C ]//Proceedings of the 20th ACM International Conterenee on Multimedia. NewYork, USA: ACM, 2012: 1133-1136. [ DOI: 10. 1145/ 2393347. 2396401 ].

二级参考文献10

  • 1J Schafer,J Konstan,J Riedl.Recommender systems in e-commerce[C].In:Proc of ACM E-Commerce.New York:ACM Press,1999.158-166
  • 2Champa Jayawardana,K Priyantha Hewagamage Masashito HIrakawa.A personalize information environment for digital libraries[J].Information Technology and Libraries,2001,20(4):185-196
  • 3J Konstan,B Miller,D Maltz,et al.GroupLens:Applying collaborative filtering to Usenet news[J].Communications of the ACM,1997,40(3):77-87
  • 4高凤荣.个性化推荐系统关键技术研究:[博士论文][D].北京:中国人民大学,2003
  • 5Greg Linden,Brent Smith,Jeremy York.Amazon.com recommendations:Item-to-Item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80
  • 6G Adomavicius,A Tuzhilin.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J].IEEE Trans on Knowledge and Data Engineering,2005,17(6):734-749
  • 7Chun Zeng,Chun-Xiao Xing,Li-Zhu Zhou,et al.Similarity measure and instance selection for collaborative filtering international[J].Journal of Electronic Commerce,2004,4(8):115-129
  • 8G Karypis.Evaluation of item-based top-N recommendation algorithms[C].In:Proc of CIKM 2001.New York:ACM Press,2001.247-254
  • 9Brendan Kitts,David Freed,Martin Vrieze.Cross-sell:A fast promotion-tunable customer-item recommendation method based on conditional independent probabilities[C].In:Proc of ACM SIGKDD Int'l Conf.New York:ACM Press,2000.437-446
  • 10KDD2000 Dataset[OL].http://www.ecn.purdue.edu/KDDCUP/data/,2005

共引文献147

同被引文献34

  • 1Jones S, Shao L. Action retrieval with relevance feedback on youtube videos [ J ] Proceedings of the Third International Conference on Intemet Multimedia Computing and Service. ACM, 2011,42-45.
  • 2Wang H, Klaser A, Schmid C, et al. Action recognition by dense trajectories[ J] Computer Vision and Pattern Recogni- tion (CVPR), 2011 IEEE Conference on. IEEE, 2011, 3169-3176.
  • 3Yang Y, Zhuang Y, Tao D, et al. Recognizing cartoon im- age gestures for retrieval and interactive cartoon clip synthe- sis [ J ]. Circuits and Systems for Video Technology, IEEE Transactions on, 2010, 20(12) : 1745-1756.
  • 4Reddy K K, Shah M. Recognizing 50 human action catego- ries of web videos [ J ]. Machine Vision and Applications, 2013, 24(5) : 971-981.
  • 5Golparvar-Fard M, Heydarian A, Niebles J C. Vision- based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classi- tiers [ J ]. Advanced Engineering Informatics, 2013, 27 (4) : 652-663.
  • 6Jones S, Shao L. Rapid localisation and retrieval of human actions with relevance feedback[ C ] Springer Berlin Heidel- berg: Computer Analysis of Images and Patterns. 2013.
  • 7Shao L, Chen X. Histogram of Body Poses and Spectral Re- gression Discriminant Analysis for Human Action Categori- zation[J] BMVC, 2010, 1-11.
  • 8Wang J, Yang J, Yu K, et al. Locality-constrained linear coding for image classification [ J ] Computer Vision and Pattern Recognition ( CVPR), 2010 IEEE Conference on. IEEE, 2010, 3360-3367.
  • 9Tan J S F, Lu E H C,Tseng VS, et al. Preference -oriehted min-ing techniques for location-based store search [J], Knowledge andInformation Systems, 2013 , 34( 1) : 147 - 169.
  • 10Bian Jiang, Long Bo, Li Lihong,et al. Exploiting user preferencefor online learning in Web content optimization systems [ EB/OL ].[2015 - 04 - 18]. http://dl. acm. org/citation, cfm. id =2493259.

引证文献2

二级引证文献25

中国图象图形学报
2014年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部