Improving the Accuracy of the Video Popularity Prediction Models through User Grouping and Video Popularity Classification

This paper proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for "user grouping" and "content classification". The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 number of BBC iPlayer users. Using the proposed grouping technique, user groups of similar interest and up to 2 video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art including SH, ML, MRBF models on average by 45%, 33% and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising and video broadcasting technologies benefit from our findings to illustrate the implications