ViViD: View Prediction of Online Video Through Deep Neural Network-Based Analysis of Subjective Video Attributes

Popularity of a video in an online platform may be defined by its number of views. The total view count of a video may change throughout its presence in an online platform. However, in most cases the view count tends to saturate after a certain time. We propose a method to predict the total view of a video at saturation. We have modeled the task of finding the view count of a video at saturation as a joint classification and regression problem, which is solved via a deep neural network. The network has a classification and a regression head. The classification head decides the view band among a set of available bands, whereas the regression head outputs a tolerance view count within each band. We consider four video attributes as the input to the network, namely, the thumbnail associated with the video, the title, the audio and the video itself for the view prediction task. The attributes are fused in a hierarchical fashion in the deep neural network. We propose a custom mismatch loss function and a penalty loss function for the joint training of the classification and regression heads of the network. Experimental results show that our method is 6.47% better in view prediction than the competitive methods.