A Heterogeneous Learning Framework for Over-the-Top Consumer Analysis Reflecting the Actual Market Environment

The over-the-top (OTT) market for media consumption over wired and wireless Internet is growing. It is, therefore, crucial that service providers and carriers participating in the OTT market analyze consumer traffic for pricing, service delivery, infrastructure investments, etc. The OTT market has many consumer groups, but the proportion of users is not consistent in each. Furthermore, as multimedia consumption has increased owing to the COVID-19 epidemic, the OTT market has changed rapidly. If this is not reflected, the analysis will not be accurate. Therefore, we propose a framework that can classify consumers well based on actual OTT market environment conditions. First, by applying our proposed conditional probability-based method to basic machine learning techniques, such as support vector machine, k-nearest neighbor, and decision tree, we can improve the classification performance, even for an imbalanced OTT consumer distribution. Then, it is possible to analyze the changing consumer trends by dynamically retraining the incoming OTT consumer data. Conventional methods result in low classification accuracy in low-number classes, but our method shows an improvement of 5.3-19.2% based on recall. Moreover, conventional methods have shown large fluctuations in performance as the OTT market environment has changed, but our framework consistently maintains high performance.

Automated summary

The OTT market for media consumption over wired and wireless Internet is growing, leading to the need for service providers and carriers to analyze consumer traffic for pricing, service delivery, etc. With an inconsistent user proportion among various consumer groups and the rapid changes in the market due to COVID-19, it is important to accurately classify consumers and adapt to changing trends. A proposed framework utilizes a conditional probability-based method to improve classification performance, even for an imbalanced consumer distribution, and dynamically retrain incoming consumer data to analyze shifting trends in the market. This framework shows significant improvements in classification accuracy compared to conventional methods, maintaining high performance despite fluctuations in the OTT market environment.