With the surging demand on high-quality mobile video services and the unabated development of new network technology,\nincluding fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for\nvarious network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal\nfor investors and advertisers.Therefore, many works have focused on understanding how the factors, especially quality of service\n(QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled\nfrom QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well\nas feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first\npropose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our\nempirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model.\nThrough experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over\nthe baseline model which only considers QoS factors.The proposed model has potential for designing QoE-oriented scheduling\nstrategies in various network scenarios, especially in the fog computing context.
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