Leveraging recommendation systems for improving caching emerging short video in content delivery network

Abstract

AbstractShort video applications have rapidly become one of the most popular online entertainments. It is also one of the most important ways of social networking. However, the request pattern of short videos shows the characteristics of highly dynamic compared to traditional video. First, the access of short video is changing quickly, that is, user shows less preferences. Second, most short videos have no tag descriptions. These result in a significant reduction in caching efficiency of content delivery network (CDN) server, which is used to cache short videos. To address these challenges, this article proposes a recommendation system to optimize short video caching. For lack of tag description, this article proposes event detection method to recognize tags based on improved dense trajectory and concept dictionary video feature extraction. Then the collaborative filtering method is used to generate caching recommendation list for CDN to guide short video caching. Simulation results show that the proposed method can significantly improve the efficiency of short video caching for CDN.