Name
Abstract | ||
|---|---|---|
Movies are a major form of entertainment in the US. There are a dozens of websites focusing on movie information. On most of the websites, ratings and reviews from the users play an important role. When a user gives a movie a certain score, the user not only reflects his taste toward that movie but also potentially exposes his personal information. In this paper, we investigated several movie genres. In each genre, movies were classified into different clusters by using expectationmaximization (EM) algorithm. The classification criteria were built upon audience movie rating scores and existing user information. As a result, a new or anonymous users personal information could be predicted when he rated movies on movie-related websites. Moreover, newly released movies could be easily classified into corresponding clusters to assistant user information discovery. The revealed personal information was very useful and could be utilized in different ways such as increasing the accuracy for delivering user-related ads. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IIKI.2016.84 | 2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) |
Keywords | Field | DocType |
data mining,movie,website privacy | World Wide Web,Computer security,Computer science,Entertainment,Film genre,User information,Personally identifiable information,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-5090-5953-9 | 0 | 0.34 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Mei | 1 | 40 | 3.03 |
| Xiaolu Cheng | 2 | 8 | 3.61 |
| Xiaoshuang Xing | 3 | 257 | 14.07 |
| Bowu Zhang | 4 | 83 | 6.83 |
| Wei Cheng | 5 | 811 | 106.56 |