The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications ...The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.展开更多
在数字芯片设计后端流程中,宏和标准单元的布局是一项耗时的工作,通过机器学习快速有效地提供解决方案能够加快芯片开发的周期,降低人工布局带来的风险;然而布局问题是一个多目标优化问题,目前大多数方法都注重在满足各项指标下最大化...在数字芯片设计后端流程中,宏和标准单元的布局是一项耗时的工作,通过机器学习快速有效地提供解决方案能够加快芯片开发的周期,降低人工布局带来的风险;然而布局问题是一个多目标优化问题,目前大多数方法都注重在满足各项指标下最大化减小线长,已换取时钟延迟的降低,忽略了其他指标仍然存在下降的空间,例如良好的拥塞指标有利于降低芯片散热和功耗;针对上述问题,设计一种新的带有密集型奖励函数的深度强化学习框架,将拥塞信息映射到图像中,给出新的特征嵌入模型对版图的全局信息进行多尺度提取,并引入图注意力网络捕获网表的连接关系,采用Advantage Actor Critic(A2C)算法更新策略函数,实现了数字版图的自动布局,并在公共的数字芯片网表基准上验证了该方法的有效性。展开更多
基金supported by National Key R&D Program of China(No.2022YFB3104500)Natural Science Foundation of Jiangsu Province(No.BK20222013)Scientific Research Foundation of Nanjing Institute of Technology(No.3534113223036)。
文摘The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.
文摘在数字芯片设计后端流程中,宏和标准单元的布局是一项耗时的工作,通过机器学习快速有效地提供解决方案能够加快芯片开发的周期,降低人工布局带来的风险;然而布局问题是一个多目标优化问题,目前大多数方法都注重在满足各项指标下最大化减小线长,已换取时钟延迟的降低,忽略了其他指标仍然存在下降的空间,例如良好的拥塞指标有利于降低芯片散热和功耗;针对上述问题,设计一种新的带有密集型奖励函数的深度强化学习框架,将拥塞信息映射到图像中,给出新的特征嵌入模型对版图的全局信息进行多尺度提取,并引入图注意力网络捕获网表的连接关系,采用Advantage Actor Critic(A2C)算法更新策略函数,实现了数字版图的自动布局,并在公共的数字芯片网表基准上验证了该方法的有效性。
