DatasetAdded By Infochimps
To support the investigation of communal data structures, such as folksonomies, in the context of recommendation, we have created a large knowledge base about movies and how users rate movies. To achieve this, a large portion of the Internet Movie Database (IMDB) was downloaded from to provide information about movies, actors and production personnel, as well a large set of keywords that have been assigned by users to describe movies. The IMDB dataset contains 898,078 movie titles, 2,564,990 names (including actors, actresses, writers, directors and producers), and 32,247 keywords. To obtain information about the way users rate movies, we have collected a dataset from Netflix, a mail-based movie rental company in the US, which contains the movie ratings of 480,189 customers across 17,770 movie titles over the last five years.
Both the IMDB and Netflix datasets have been converted into a relational database, a 643MB compressed MySQL dump. To provide a single view over both datasets, for example, to support the querying of information on movies from IMDB and how users rate these movies from Netflix, we have correlated the 13,880 movie titles in the Netflix dataset with their IMDB counterparts. The result is a large knowledge base on movies and movie ratings that supports semantic querying (for example through SPARQL). The mappings between movie titles in Netflix with those in IMDB can be downloaded from here.