netflix_prize — Netflix Prize ratings (1998-2005)

Description

Bipartite networks of the customers and movies of the DVD rental and streaming service Netflix, from the data set that Netflix released in October 2006 for the Netflix Prize, an open competition for the best algorithm to predict user ratings of movies. The data set contains 100,480,507 ratings, from 1 to 5 stars (whole stars only), that 480,189 customers, chosen at random, gave to 17,770 movies between November 1999 and December 2005 (October 1998 to December 2005, according to the documentation of the data set), with the date of each rating. The customer ids were replaced by random ids. The movies are given with their titles and years of release (which may be those of the DVD release), and their ids are not those used by Netflix.

training: the training set of the competition, where each edge is the rating that a customer gave to a movie (a customer rates a movie at most once). The 1,408,395 ratings of the probe set, a subset of the training set that has the same properties as the qualifying set, are marked.
qualifying: the qualifying set of the competition, 2,817,131 pairs of customers and movies with the dates of the ratings (from January 2000 to December 2005), whose ratings were withheld (the ratings that the competitors had to predict). These ratings were never released.

Both networks contain all the customers and movies of the data set, in the same order.

Shortly after the release, Narayanan and Shmatikov showed that some of the customers in the data set could be re-identified by matching their ratings and dates with public ratings on the Internet Movie Database (IMDb). In December 2009, a class-action lawsuit was filed against Netflix over the release of the data, and in March 2010 Netflix settled it and canceled a planned second competition, which was to use data with demographic information; the release of the data to the UCI Machine Learning Repository after the competition, announced in its rules, did not take place, and Netflix no longer distributes the data set. The files of the upstream source used here are a copy of the original data set uploaded to the Internet Archive in 2016.

The networks have the following properties:

Nodes: is_user, whether the node is a customer (otherwise a movie); user_id, the anonymized id of the customer (-1 for movies); movie_id, the id of the movie in the data set (-1 for customers); title and year, the title of the movie and its year of release (-1 if not given, and for customers).
Edges: time, the date of the rating, as Unix time (at midnight UTC).
training, edges: rating, the rating in stars; probe, whether the rating is in the probe set.

The files of this entry are available only with an access token, since the license of the data set forbids redistributing it without permission from Netflix.

Tags

Social Online Preferences Weighted Timestamps Metadata

Citation

J. Bennett, S. Lanning, "The Netflix Prize." Proceedings of KDD Cup and Workshop 2007, 3-6 (2007) www.cs.uic.edu

Upstream URL OK

https://archive.org/details/nf_prize_dataset.tar

Upstream license

Restricted access. Upstream does not permit data redistribution, hence the network files cannot be downloaded without authorization. See here how to gain access.

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
training	497,959	100,480,507	403.57	3363.30	5022.55	1.43	-0.39	0.00	5	1.00	Undirected	Bipartite	is_user user_id movie_id year title	rating time probe	370.9 MiB	787.2 MiB	743.4 MiB	698.7 MiB
qualifying	497,959	2,817,131	11.31	143.77	167.61	3.94	-0.23	0.00	9	1.00	Undirected	Bipartite	is_user user_id movie_id year title	time	19.3 MiB	31.0 MiB	29.6 MiB	27.2 MiB