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
新闻点击率预估是个性化新闻推荐的关键技术之一。针对现有新闻点击率预估方法忽略新闻全局特征建模及信息压缩导致语义丢失的问题,提出融合多模态和长短期历史行为的新闻点击率预估模型(MLSTH:Multimodal and Long-Short Term Historic...新闻点击率预估是个性化新闻推荐的关键技术之一。针对现有新闻点击率预估方法忽略新闻全局特征建模及信息压缩导致语义丢失的问题,提出融合多模态和长短期历史行为的新闻点击率预估模型(MLSTH:Multimodal and Long-Short Term Historical Behavior News Click-Through Rate Prediction)。MLSTH主要包括新闻编码和用户编码两部分。在新闻编码中,首先利用预训练模型对新闻多模态特征编码;然后,基于跨模态注意力构建视觉语义融合模块分别得到全局特征信息和局部特征信息;最后,将得到的局部特征和全局特征拼接,作为多模态新闻编码。通过在公开数据集V-MIND上验证,与现有多模态模型MMRec, VLSNR,IM-Rec相比较,AUC平均提升1.68%,2.54%和2.36%,证明了其有效性和优越性。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
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。
