网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的...网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。展开更多
Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inc...Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.展开更多
文摘网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。
基金supported by the National Natural Science Foundation of China (No.31872399)Advantage Discipline Construction Project (PAPD,No.6-2018)of Jiangsu University。
文摘Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.
