The Internet Movie Database

By Cherie' L. Weible. 2001

442 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Library and Information Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Michael W. Mahoney
2. Shrikanth S. Narayanan
3. Hongwei Ng
4. Felix Borutta
5. Evgheniy Faerman
6. Anil Ramakrishna
7. Nikos Malandrakis
8. Stefan Winkler

In Information Systems

1. Michael W. Mahoney
2. Shrikanth S. Narayanan
3. Hongwei Ng
4. Felix Borutta
5. Evgheniy Faerman
6. Anil Ramakrishna
7. Nikos Malandrakis
8. Stefan Winkler

In Library and Information Sciences

1. Shrikanth S. Narayanan
2. Hongwei Ng
3. Anil Ramakrishna
4. Nikos Malandrakis
5. Stefan Winkler
6. Elizabeth Staruk

Citations in the corpus

• A quantitative analysis of gender differences in movies using psycholinguistic normatives (2015, methodology): “Additional metadata for each movie were collected from the Internet Movie Database (IMDb, 2015).”
• LASAGNE: Locality and Structure Aware Graph Node Embedding (2017, background): “• IMDb Germany: This kind of artificial dataset is created from the IMDb movie database [38].”
• A data-driven approach to cleaning large face datasets (2014, methodology): “We include a term in the objective function to label faces in X (i) with a different gender from the ith query as outliers.”