中国综合性科技类核心期刊(北大核心)

中国科学引文数据库来源期刊(CSCD)

美国《化学文摘》(CA)收录

美国《数学评论》(MR)收录

俄罗斯《文摘杂志》收录

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于概率图模型的互联网广告点击率预测

岳昆 王朝禄 朱运磊 武浩 刘惟一

岳昆, 王朝禄, 朱运磊, 武浩, 刘惟一. 基于概率图模型的互联网广告点击率预测[J]. 华东师范大学学报(自然科学版), 2013, (3): 15-25.
引用本文: 岳昆, 王朝禄, 朱运磊, 武浩, 刘惟一. 基于概率图模型的互联网广告点击率预测[J]. 华东师范大学学报(自然科学版), 2013, (3): 15-25.
YUE Kun, WANG Chao-lu, ZHU Yun-lei, WU Hao, LIU Wei-yi. Click-through rate prediction of online advertisements based on probabilistic graphical model[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 15-25.
Citation: YUE Kun, WANG Chao-lu, ZHU Yun-lei, WU Hao, LIU Wei-yi. Click-through rate prediction of online advertisements based on probabilistic graphical model[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 15-25.

基于概率图模型的互联网广告点击率预测

详细信息
  • 中图分类号: TP311

Click-through rate prediction of online advertisements based on probabilistic graphical model

  • 摘要: 点击率预测可以提高用户对所展示互联网广告的满意度,支持广告的有效投放,是针对用户进行广告的个性化推荐的重要依据.对于没有历史点击记录的用户,仍需对其推荐广告,预测所推荐广告的点击率.针对这类用户,以贝叶斯网这一重要的概率图模型,作为不同用户之间广告搜索行为的相似性及其不确定性的表示和推理框架,通过对用户搜索广告的历史记录进行统计计算,构建反映用户间相似关系的贝叶斯网,进而基于概率推理机制,定量度量没有历史点击记录的用户与存在历史点击记录的用户之间的相似性,从而预测没有历史点击记录的用户对广告的点击率,为广告推荐提供依据.通过建立在KDD Cup 2012-Track 2的Tencent CA训练数据集上的实验,测试了方法的有效性.
  • [1] [1] 周傲英,周敏奇,宫学庆.计算广告:以数据为核心的Web综合应用 [J]. 计算机学报, 2011, 34(10): 1805-1819.

    [2] REGELSON M, FAIN D. Predicting click-through rate using keyword clusters[C]//Proceedings of the Second Workshop on Sponsored Search Auctions, EC 2006. Michigan: ACM, 2006.

    [3] AGARWAL D,BRODER A, CHAKRABARTI D, et al. Estimating rates of rare events at multiple resolutions. Proceedings of the ACM SIGMOD International Conference on Management of Data. Beijing: ACM, 2007: 16-25.

    [4] RICHARDSON M, DOMINIWSKA E, RAGNO R. Predicting Clicks: Estimating the Click-Through Rate for New Ads[C]//Proceedings of the 16th International Conference on World Wide Web, WWW 2007. Banff: ACM, 2007: 521-530.

    [5] CHAKRABARTI D, AGARWAL D, JOSIFOVSKI V. Contextual Advertising by Combining Relevance with Click Feedback[C]//Proceedings of the 17th International Conference on World Wide Web, WWW 2008. Beijing: ACM, 2008: 417-426.

    [6] GOLLAPUDI S, PANIGRAHY R, GOLDSZMIDT M. Inferring Clickthrough Rates on Ads from Click Behavior on Search Results[C]//Proceedings of the Workshop on User Modeling for Web Applications, Fourth International Conference on Web Search and Web Data Mining, WSDM 2011. Hong Kong: ACM,2011.

    [7] YAN J, LIU N, WANG G, et al. How much can Behavioral Targeting Help Online Advertising?[C]//Proceedings of the 18th International Conference on World Wide Web, WWW 2009. Madrid: ACM, 2009: 261-270.

    [8] AHMED A, LOW Y, ALY M, et al. Scalable distributed inference of dynamic user interests for behavioral targeting[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, CA: ACM, 2011: 114-122.

    [9] WANG X, LI W, CUI Y, et al. Click Through Rate Estimation for Rare Events in Online Advertising. Online Multimedia Advertising: Techniques and Technologies, Chapter1 [M/OL]. 2011[2012-06-15]. http://labs.yahoo.com/node/434.

    [10] PEARL J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference [M]. San Mateo, CA: Morgan Kaufmann Publishers, 1988.

    [11] RUSSEL S, NORVIG P. Artificial Intelligence—A Modern Approach [M]. Boston: Pearson Education, Publishing as Prentice-Hall, 2002.

    [12] DEMBCZYNSKI K, KOTLOWSKI W, WEISS D. Predicting Ads’ Click Through Rate with Decision Rules. [EB/OL]. 2008-03-31[2012-06-15].Yahoo Research, http://research.yahoo.com/workshops/troa-2008/papers/ submission_12.pdf.

    [13] GRAEPEL T, BORCHERT T, HERBRICH R, et al. Probabilistic Machine Learning in Computational Advertising Microsoft Research [EB/OL]. 2010-12-10 [2012-06-15]. http://research.microsoft.com/en-us/um/ beijing/events/mload-2010/.

    [14] CHAPELLE O, ZHANG Y. A dynamic Bayesian network click model for web search ranking[C]//Proceedings of the 18th International Conference on World Wide Web, WWW 2009. Madrid: ACM, 2009: 1-10.

    [15] 张少中,高飞.一种基于小世界网络和贝叶斯网的混合推荐模型 [J]. 小型微型计算机系统, 2010, 31(10): 1974-1978.

    [16] HRYCEJ T. Gibbs sampling in Bayesian networks [J]. Artificial Intelligence, 1990, 46: 351-363.

    [17] PEARL J. Evidential reasoning using stochastic simulation of causal models [J]. Artificial Intelligence, 1987, 32: 245-257.

    [18] KDD CUP 2012 Track 2: Predict the click-through rate of ads given the query and user information [EB/OL].2012-02-20[2012-06-15]. http://www. kddcup2012.org/c/kddcup2012-track2.
  • 加载中
计量
  • 文章访问数:  5324
  • HTML全文浏览量:  5
  • PDF下载量:  6445
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-03-01
  • 修回日期:  2013-04-01
  • 刊出日期:  2013-05-25

基于概率图模型的互联网广告点击率预测

  • 中图分类号: TP311

摘要: 点击率预测可以提高用户对所展示互联网广告的满意度,支持广告的有效投放,是针对用户进行广告的个性化推荐的重要依据.对于没有历史点击记录的用户,仍需对其推荐广告,预测所推荐广告的点击率.针对这类用户,以贝叶斯网这一重要的概率图模型,作为不同用户之间广告搜索行为的相似性及其不确定性的表示和推理框架,通过对用户搜索广告的历史记录进行统计计算,构建反映用户间相似关系的贝叶斯网,进而基于概率推理机制,定量度量没有历史点击记录的用户与存在历史点击记录的用户之间的相似性,从而预测没有历史点击记录的用户对广告的点击率,为广告推荐提供依据.通过建立在KDD Cup 2012-Track 2的Tencent CA训练数据集上的实验,测试了方法的有效性.

English Abstract

岳昆, 王朝禄, 朱运磊, 武浩, 刘惟一. 基于概率图模型的互联网广告点击率预测[J]. 华东师范大学学报(自然科学版), 2013, (3): 15-25.
引用本文: 岳昆, 王朝禄, 朱运磊, 武浩, 刘惟一. 基于概率图模型的互联网广告点击率预测[J]. 华东师范大学学报(自然科学版), 2013, (3): 15-25.
YUE Kun, WANG Chao-lu, ZHU Yun-lei, WU Hao, LIU Wei-yi. Click-through rate prediction of online advertisements based on probabilistic graphical model[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 15-25.
Citation: YUE Kun, WANG Chao-lu, ZHU Yun-lei, WU Hao, LIU Wei-yi. Click-through rate prediction of online advertisements based on probabilistic graphical model[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 15-25.
参考文献 (1)

目录

    /

    返回文章
    返回