Name
Abstract | ||
|---|---|---|
Predicting the top-N popular videos and their future views for a large batch of newly uploaded videos is of great commercial value to online video services (OVSs). Although many attempts have been made on video popularity prediction, the existing models has a much lower performance in predicting the top-N popular videos than that of the entire video set. The reason for this phenomenon is that most... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMM.2018.2845688 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Videos,Predictive models,Market research,Data models,Task analysis,YouTube | Data modeling,Task analysis,Pattern recognition,Computer science,Popularity,Upload,Bayesian multivariate linear regression,Artificial intelligence,Predictive modelling,Discriminative model,Market research,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 1 | 1520-9210 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
2 | ||