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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.
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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

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