Name
Abstract | ||
|---|---|---|
All large-scale online video systems, for example, Netflix and Youku, make a significant investment on video recommendations that can dramatically affect video information diffusion processes among users. However, there is a lack of efficient methodology to interpret how various recommendation mechanisms affect information diffusion processes resulting in the difficulty to evaluate video recommend... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMM.2017.2781364 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Diffusion processes,Sociology,Statistics,Electronic mail,Australia,Analytical models,YouTube | Computer vision,Epidemic model,Information retrieval,Computer science,Artificial intelligence,Online video | Journal |
Volume | Issue | ISSN |
20 | 8 | 1520-9210 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yipeng Zhou | 1 | 278 | 33.33 |
| Jiqiang Wu | 2 | 9 | 1.80 |
| Terence H. Chan | 3 | 60 | 10.01 |
| Siu-Wai Ho | 4 | 195 | 26.35 |
| Dah Ming Chiu | 5 | 3618 | 694.24 |
| Di Wu | 6 | 1222 | 78.55 |