Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article,  report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data,  use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction.  propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data,  confirm high performance in user activity level prediction. In addition,  show that the inferred topics indicate that future activity level anomalies correlate highly with early sked bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell tors in the future.
['Chen Luo', 'Jia Zeng', 'Mingxuan Yuan', 'Wenyuan Dai', 'Qiang Yang']
I am writing a research paper on a idea Telco User Activity Level Prediction with Massive Mobile Broadband Data
[{"from": "human", "value": "I am writing a research paper on a idea Telco User Activity Level Prediction with Massive Mobile Broadband Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco\u2019s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article,  report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal \u201cwords\u201d that express user behavior. By extracting users\u2019 space-time access records in MBB data,  use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction.  propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data,  confirm high performance in user activity level prediction. In addition,  show that the inferred topics indicate that future activity level anomalies correlate highly with early sked bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell tors in the future."}]