Analyzing and Predicting the Popularity of Online Contents
Minh‐Tri Nguyen, Takuma Nakajima, Masato Yoshimi, Nam Thoai · 2019
With the rapid growth of Internet technology and infrastructure, we have entered the era of data explosion. Following this is the emergence of social networks, which have brought an enormous and ever-growing amount of online content into our digital world. Knowing precisely the popularity of online contents is of great importance for developing advanced caching algorithms as well as content distribution strategies. In this study, we provide some crucial insights into the characteristics of online content popularity over time in different locations and propose a simple predictive model to estimate the popularity of online contents in particular periods. By experiencing with the real datasets of MovieLens and Youtube, our model not only achieves considerable accuracy but also shows an impressive reduction in computation time, from 80 to 250 times faster comparing to some baseline methods. At last, we also provide the potentials and limitations of our model in practice.