Online advertisements have a significant influence over the success or failure of your business.Therefore,it is important to somehow measure the impact of your advertisement before uploading it online,and this is can ...Online advertisements have a significant influence over the success or failure of your business.Therefore,it is important to somehow measure the impact of your advertisement before uploading it online,and this is can be done by calculating the Click Through Rate(CTR).Unfortunately,this method is not eco-friendly,since you have to gather the clicks from users then compute the CTR.This is where CTR prediction come in handy.Advertisement CTR prediction relies on the users’log regarding click information data.Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days.CTR prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client:The more clicks,the more successful the ad is.In this study we develop a machine learning based click through rate prediction model.The proposed study defines a model that generates accurate results with low computational power consumption.We used four classification techniques,namely K Nearest Neighbor(KNN),Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).The study was performed on the Click-Through Rate Prediction Competition Dataset.It is a click-through data that is ordered chronologically and was collected over 10 days.Experimental results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features.展开更多
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...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.展开更多
点击率预测是计算广告学的核心算法之一。传统浅层模型没有充分考虑到数据之间存在的非线性关系,且使用人工特征提取方法费时费力。针对这些问题,提出了基于卷积(Convolutional Neural Networks)-LSTM(Long Short Term Memory)混合神经...点击率预测是计算广告学的核心算法之一。传统浅层模型没有充分考虑到数据之间存在的非线性关系,且使用人工特征提取方法费时费力。针对这些问题,提出了基于卷积(Convolutional Neural Networks)-LSTM(Long Short Term Memory)混合神经网络的广告点击率预测模型。该模型使用卷积神经网络提取高影响力特征,并通过LSTM神经网络的时序性进行预测分类。实验结果证明:与浅层模型或单一结构的神经网络模型相比,基于卷积-LSTM的混合神经网络模型能有效提高广告点击事件的预测准确率。展开更多
当前主流的在线广告点击率(CTR)预估算法主要通过机器学习方法从大规模日志数据中挖掘用户与广告间的相关性从而提升点击率预估精度,其不足之处在于没有充分考虑用户实时行为对CTR的影响。对大规模真实在线广告日志进行分析后发现,在会...当前主流的在线广告点击率(CTR)预估算法主要通过机器学习方法从大规模日志数据中挖掘用户与广告间的相关性从而提升点击率预估精度,其不足之处在于没有充分考虑用户实时行为对CTR的影响。对大规模真实在线广告日志进行分析后发现,在会话中,用户CTR的动态变化和用户先前的反馈行为高度相关,不同的用户行为对用户实时CTR的影响不尽相同。基于上述分析结果,提出一种基于用户实时反馈的点击率预估算法。首先,从大规模真实在线广告日志数据中定量分析用户反馈和点击率预估精度的相关关系;然后,根据分析结果将用户的反馈行为特征化;最后,使用机器学习方法对用户的行为进行建模,并根据用户的反馈实时动态调整广告投放,从而提升在线广告系统的点击率预估精度。实验结果表明,用户实时反馈特征和用户点击率高度相关;相比于传统没有用户实时反馈信息的预测模型,该算法在测试集上对AUC(Area Under the Curve)和RIG(Relative Information Gain)指标提升分别为0.83%和6.68%。实验结果表明,用户实时反馈特征显著提高点击率预估的精度。展开更多
文摘Online advertisements have a significant influence over the success or failure of your business.Therefore,it is important to somehow measure the impact of your advertisement before uploading it online,and this is can be done by calculating the Click Through Rate(CTR).Unfortunately,this method is not eco-friendly,since you have to gather the clicks from users then compute the CTR.This is where CTR prediction come in handy.Advertisement CTR prediction relies on the users’log regarding click information data.Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days.CTR prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client:The more clicks,the more successful the ad is.In this study we develop a machine learning based click through rate prediction model.The proposed study defines a model that generates accurate results with low computational power consumption.We used four classification techniques,namely K Nearest Neighbor(KNN),Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).The study was performed on the Click-Through Rate Prediction Competition Dataset.It is a click-through data that is ordered chronologically and was collected over 10 days.Experimental results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features.
文摘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.
文摘点击率预测是计算广告学的核心算法之一。传统浅层模型没有充分考虑到数据之间存在的非线性关系,且使用人工特征提取方法费时费力。针对这些问题,提出了基于卷积(Convolutional Neural Networks)-LSTM(Long Short Term Memory)混合神经网络的广告点击率预测模型。该模型使用卷积神经网络提取高影响力特征,并通过LSTM神经网络的时序性进行预测分类。实验结果证明:与浅层模型或单一结构的神经网络模型相比,基于卷积-LSTM的混合神经网络模型能有效提高广告点击事件的预测准确率。
文摘当前主流的在线广告点击率(CTR)预估算法主要通过机器学习方法从大规模日志数据中挖掘用户与广告间的相关性从而提升点击率预估精度,其不足之处在于没有充分考虑用户实时行为对CTR的影响。对大规模真实在线广告日志进行分析后发现,在会话中,用户CTR的动态变化和用户先前的反馈行为高度相关,不同的用户行为对用户实时CTR的影响不尽相同。基于上述分析结果,提出一种基于用户实时反馈的点击率预估算法。首先,从大规模真实在线广告日志数据中定量分析用户反馈和点击率预估精度的相关关系;然后,根据分析结果将用户的反馈行为特征化;最后,使用机器学习方法对用户的行为进行建模,并根据用户的反馈实时动态调整广告投放,从而提升在线广告系统的点击率预估精度。实验结果表明,用户实时反馈特征和用户点击率高度相关;相比于传统没有用户实时反馈信息的预测模型,该算法在测试集上对AUC(Area Under the Curve)和RIG(Relative Information Gain)指标提升分别为0.83%和6.68%。实验结果表明,用户实时反馈特征显著提高点击率预估的精度。
