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基于细粒度标签的在线视频广告投放机制研究
  • 摘要

    随着互联网的发展,对精彩视频点进行标注、评论和分享成为趋势.这类群体智慧信息的有效利用将有助于提升视频广告的投放效果.首先将用户提供的细粒度视频标签收集起来,通过视频时间轴加权计算生成视频热点,进而利用视频热点描述信息基于分类匹配的思想来选取广告,最后找出视频热点内用户对视频关注度下降幅度最大的时间点投放广告.实验证明,在数量为百万级的视频集合中,该方法选取的广告与视频的相关性达到85%左右.用户在广告播放过程中关闭广告的概率小于10%.与目前广泛应用的广告投放方式相比,广告的平均播放时间能提升21.5%,广告点击率能从0.65%提高至0.73%.

  • 作者

    陆枫  王子锐  廖小飞  金海  Lu Feng  Wang Zirui  Liao Xiaofei  Jin Hai 

  • 作者单位

    服务计算技术与系统教育部重点实验室(华中科技大学) 武汉430074/集群与网格计算湖北省重点实验室(华中科技大学) 武汉430074

  • 刊期

    2014年12期 ISTIC EI PKU

  • 关键词

    在线视频广告  细粒度视频标签  视频热点  机器学习  定向广告  online video advertising  fine-grained video tags  video hotspots  machine learning  target advertising 

参考文献
  • [1] 王家卓,刘奕群,马少平,张敏. 基于用户行为的竞价广告效果分析. 计算机研究与发展, 2011,1
  • [2] 俞淑平,陈刚. 一种高效的行为定向广告投放算法. 计算机应用与软件, 2011,1
  • [3] iLoveI. eMarketer:网络视频广告进入主流舞台. http://www.199it.com/archives/114748.html, 2013-05-13
  • [4] Ames M;Naaman M. Why we tag:Motivations for annotation in mobile and online media. New York:ACM, 2007
  • [5] Nov O;Ye C. Why do people tag:Motivations for photo tagging. Communications of the ACM, 2010,07
  • [6] Velsen L;Melenhorst M. Incorporating user motivations to design for video tagging. Interact with Computers, 2009,03
  • [7] Held C;Kimmerle J;Cress U. Learning by foraging:The impact of individual knowledge and social tags on web navigation processes. COMPUTERS IN HUMAN BEHAVIOR, 2012,01
  • [8] Chatterjee P;Hoffman D L;Novak T P. Modeling the clickstream:Implications for web-based advertising efforts. MARKETING SCIENCE, 2003,04
  • [9] Mccoy S;Everard A;Polak P. The effects of online advertising. Communications of the ACM, 2007,03
  • [10] Palda K S. The measurement of cumulative advertising effects. Journal of Business, 2012,02
  • [11] Ribeiro-Neto B;Cristo M;Golgher P B. Impedance coupling in content-targeted advertising. New York:ACM, 2005
  • [12] Shatnawi M;Mohamed N. Statistical techniques for online personalized advertising:A survey. New York:ACM, 2012
  • [13] Murdock V;Ciaramita M;Plachouras V. A noisy channel approach to contextual advertising. New York:ACM, 2007
  • [14] Baeza-Yates R;Riberio-Neto B. Modern Information Retrieval. New York:ACM, 1999
  • [15] Siersdorfer S;Pedro J S;Sanderson M. Automatic video tagging using content redundancy. New York:ACM, 2009
  • [16] Lacerda A;Cristo M;Goncalves M A. Learning to advertise. New York:ACM, 2006
  • [17] YanJun;Liu Ning;Wang Gang. How much can behavioral targeting help online advertising. New York:ACM, 2009
  • [18] Agichtein E;Brill E;Dumais S. Improving web search ranking by incorporating user behavior information. New York:ACM, 2006
  • [19] Xue Guirong;Zeng Huajun;Chen Zheng. Optimizing web search using web click-through data. New York:ACM, 2004
  • [20] Ciaramita M;Murdock V;Plachouras V. Online learning from click data for sponsored search. New York:ACM, 2008
  • [21] Chakrabarti D;Agarwal D;Josifovski V. Contextual advertising by combining relevance with click feedback. New York:ACM, 2008
  • [22] Li Ting;Liu Ning;Yan Jun. A Markov chain model for integrating behavioral targeting into contextual advertising. New York:ACM, 2009
  • [23] Li H;Edwards S M;Lee J H. Measuring the intrusiveness of advertisements:Scale development and validation. JOURNAL OF ADVERTISING, 2002,02
  • [24] Melenhorst M;Grootveld M;Setten M. Tag-based information retrieval of video content. New York:ACM, 2008
  • [25] Rohrer C;Boyd J. The rise of intrusive online advertising and the response of user experience research at Yahoo. New York:ACM, 2004
  • [26] Vargiu E;Urru M. Exploiting web scraping in a collaborative filtering-based approach to web advertising. Artificial Intelligence Research, 2012,01
  • [27] Broder A;Marcus F;Vanja J. A semantic approach to contextual advertising. New York:ACM, 2007
  • [28] Yih W;Goodman J;Carvalho V R. Finding advertising keywords on web pages. New York:ACM, 2006
  • [29] Alam H;Hartono R;Kumar A. Web page summarization for handheld devices:A natural language approach. Piscataway,NJ:IEEE, 2003
  • [30] Rijsbergen C J;Robertson S E;Porter M F. New models in probabilistic information retrieval,5587. Cambridge:Computer Laboratory in University of Cambridge, 1980
  • [31] Wang Chingning;Zhang Ping;Choi R. Understanding consumers attitude toward advertising. 2002
  • [32] Berger A L;Mittal V O. OCELOT:A system for summarizing web pages. New York:ACM, 2000
  • [33] Anagnostopoulous A;Broder A Z;Gabrilovich E. Just-in-time contextual advertising. New York:ACM, 2007
  • [34] Wang Meng;Hong R;Li Guangda. Event driven web video summarization by tag localization and key-shot identification. IEEE Transactions on multimedia, 2012,04
  • [35] Ramos J. Using TF-IDF to determine word relevance in document queries. 2003
  • [36] Xu Mingmin;He Liang;Lin Xin. A refined TF-IDF algorithm based on channel distribution information for web news feature extraction. Piscataway,NJ:IEEE, 2010
  • [37] Yokota D;Fujita S. Article recommender for feed readers with a loss compensation based on the TF-IDF weight. Piscataway,NJ:IEEE, 2010
  • [38] Sigurbj(o)rnsson B;Zwol R. Flickr tag recommendation based on collective knowledge. New York:ACM, 2008
  • [39] Guan Ziyu;Bu Jiajun;Mei Qiaozhu. Personalized tag recommendation using graph-based ranking on multi-type.interrelated objects. New York:ACM, 2009
  • [40] Masuda T;Yamamoto D;Ohira S. Video scene retrieval using online video annotation. Berlin:Springer-Verlag, 2007
  • [41] Mei Tao;Guo Jinlian;Hua Xiansheng. AdOn:Toward contextual overlay in video advertising. Multimedia Systems, 2010,4/5
  • [42] Mei Tao;Hua Xiansheng;Yang Linjun. VideoSense:Towards effective online video advertising. New York:ACM, 2007
  • [43] Mei Tao;Hua Xiansheng;Li Shipeng. VideoSense:A contextual in-video advertising system. Circuits and Systems for Video Technology, 2009,12
  • [44] Liao Weishing;Chen Kuanting;Hsu W H. AdImage:Video advertising by image matching and ad scheduling optimization. New York:ACM, 2008
  • [45] Wan Kongwah;Xu Changsheng. Automatic content placement in sports highlights. Piscataway,NJ:IEEE, 2006
  • [46] Li Yiqun;Wan Kongwah;Yan Xin. Real time advertisement insertion in baseball video based on advertisement effect. New York:ACM, 2005
  • [47] Malheiros M;Jennett C;Patel S. Too close for comfort:A study of the effectiveness and acceptability of rich media personalized advertising. New York:ACM, 2012
  • [48] Guo Jinlian;Mei Tao;Liu Falin. AdOn:An intelligent overlay video advertising system. New York:ACM, 2009
  • [49] Richardson M;Dominowska E;Ragno R. Predicting clicks:Estimating the click-through rate for new ads. New York:ACM, 2007
  • [50] Xu Changsheng;Wan Kongwah;Bui Sonhai. Implanting virtual advertisement into broadcast soccer video. Berlin:Springer-Verlag, 2005
  • [51] Elminir H K;ElSoud M A;Sabbeh S F. Multi feature content based video retrieval using high level semantic concept. International Journal of Computer Science Issues (IJCSI), 2012,04
  • [52] Gallagher K;Foster D;Parsons J. The Medium is not the message:Advertising effectiveness and content evaluation in print and on the web. Journal of Advertising Research New York, 2001,04
  • [53] Wang Meng;Ni Bingbing;Hua Xiansheng. Assistive tagging:A survey of multimedia tagging with humancomputer joint exploration. ACM COMPUTING SURVEYS, 2012,04
  • [54] Regelson M;Fain D. Predicting click-through rate using keyword clusters. New York:ACM, 2006
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