Abstract
With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.
Highlights
The past two decades have witnessed the growth and popularity of Video-on-Demand (VoD) applications on both PCs and mobile devices, and the trend is moving from basic video offering toward better quality of user experiences (QoE) in both industry and academia [1]
In this paper we have shown that, in order to optimize user engagement in VoD streaming systems directly, an effective model of engagement incorporating both user interest and perceptual quality factors in an explicit function is needed
We have proposed an ExtractionInference (E-I) algorithm to estimate the user interest from obtained user behaviors
Summary
The past two decades have witnessed the growth and popularity of Video-on-Demand (VoD) applications on both PCs and mobile devices, and the trend is moving from basic video offering toward better quality of user experiences (QoE) in both industry and academia [1]. An engagement model based on both QoS and user interest is necessary for accurately understanding and predicting user engagement, and beneficial for optimizing system resource allocation and for providing better personalized services. The later one is inaccurate, as user engagement is sometimes not the reaction to his/her pure interest in the video and impacted by other factors, e.g., quality issues like startup delay. Another challenge is how to characterize the relationships between user engagement and the two factors in a unified model to provide insight for practical applications. We discuss the application potential of the proposed model and the future work
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