Enhancing user experience in home networks with machine learning-based classification

Abstract

With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]