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Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form <user, movie, date of grade, grade> . The user and movie fields are integer IDs, while grades are from 1 to 5 ( integer ) stars.
The grand prize of $1,000,000 was reserved for the entry which bettered Netflix's own algorithm for predicting ratings by 10%. Netflix provided a training data set of over 100 million ratings that more than 480,000 users gave to nearly 18,000 movies, which is one of the largest real-life data sets available for research.
The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
The cold start problem is a well known and well researched problem for recommender systems.Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (e-commerce, films, music, books, news, images, web pages) that are likely of interest to the user.
The Netflix Prize is particularly notable for the detailed personal information released in its dataset. Ramakrishnan et al. have conducted an extensive overview of the trade-offs between personalization and privacy and found that the combination of weak ties (an unexpected connection that provides serendipitous recommendations) and other data ...
The Netflix Prize: The Netflix Prize competition is launched by Netflix. The aim of the competition was to use machine learning to beat Netflix's own recommendation software's accuracy in predicting a user's rating for a film given their ratings for previous films by at least 10%. [39] The prize was won in 2009. 2009 Achievement ImageNet
MovieLens ratings datasets: In the early days of recommender systems, research was slowed down by the lack of publicly available datasets. In response to requests from other researchers, GroupLens released three datasets: [ 32 ] the MovieLens 100,000 rating dataset, the MovieLens 1 million rating dataset, and the MovieLens 10 million rating ...