Matrix Completion

Netflix Prize consists of about 100,000,000 ratings for 17,770 movies given by 480,189 users. Each rating in the training dataset consists of four entries: user, movie, date of grade, grade. Users and movies are represented with integer IDs, while ratings range from 1 to 5. Source: The Netflix Prize Image Source: https://www.netflixprize.com/

CAL500 (Computer Audition Lab 500) is a dataset aimed for evaluation of music information retrieval systems. It consists of 502 songs picked from western popular music. The audio is represented as a time series of the first 13 Mel-frequency cepstral coefficients (and their first and second derivatives) extracted by sliding a 12 ms half-overlapping short-time window over the waveform of each song. Each song has been annotated by at least 3 people with 135 musically-relevant concepts spanning six semantic categories: * 29 instruments were annotated as present in the song or not, * 22 vocal characteristics were annotated as relevant to the singer or not, * 36 genres, * 18 emotions were rated on a scale from one to three (e.g., not happy", neutral", ``happy"), * 15 song concepts describing the acoustic qualities of the song, artist and recording (e.g., tempo, energy, sound quality), * 15 usage terms (e.g., "I would listen to this song while driving, sleeping, etc."). Source: http://calab1.ucsd.edu/~datasets/cal500/details_cal500.txt Audio Source: http://calab1.ucsd.edu/~datasets/cal500/cal500data/

A visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. Source: SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Uses a platform with 77 candies and sweets to rank. Over 2000 users submitted over 44000 grades resulting in a matrix with 28% coverage. Source: SweetRS: Dataset for a recommender systems of sweets