Modeling and Quantifying the Forces Driving Online Video Popularity Evolution

TL;DR

This paper introduces a dynamic model for online video popularity evolution driven by recommendation forces, enabling quantification of these influences and analysis of user behavior and content popularity patterns.

Contribution

It proposes a novel dynamic popularity model based on information diffusion, fitted with real data to quantify recommendation impacts and analyze popularity trends.

Findings

Quantifies the influence of recommendation on video popularity.

Characterizes user behavior and popularity patterns.

Provides insights through a case study of TV episodes.

Abstract

Video popularity is an essential reference for optimizing resource allocation and video recommendation in online video services. However, there is still no convincing model that can accurately depict a video's popularity evolution. In this paper, we propose a dynamic popularity model by modeling the video information diffusion process driven by various forms of recommendation. Through fitting the model with real traces collected from a practical system, we can quantify the strengths of the recommendation forces. Such quantification can lead to characterizing video popularity patterns, user behaviors and recommendation strategies, which is illustrated by a case study of TV episodes.