Beyond the Content: Considering the Network for Online Video Recommendation

Abstract

Online recommendation systems play critical roles in enhancing user experience by helping them find the most interesting videos from a vast amount of content. However, the existing recommendation modules and video transmission modules in the industry often operate independently, resulting in the recommendation model providing some videos that cannot be transmitted within the specified deadlines successfully. This can lead to an inferior watching experience for users and resource waste for video providers. To address this, we propose a novel framework called NetRec, which for the first time optimizes the recommendation quality by jointly considering the network transmission. We accomplish this by re-ranking the top-N videos obtained from the recommendation system and selecting the top-M (M is approximately half of N) videos that provide the maximum overall revenue, e.g., video playing time while considering the network status. The entire system comprises network measurement, video quality estimation, and multi-objective optimization modules. Real-world Internet results show that our framework can increase users’ video playing time by 20% to 160%. Furthermore, we provide several promising directions for further improving the video recommendation quality under our NetRec framework, which jointly considers the network for the recommendation.