Recommender Systems and Personalization Datasets Julian McAuley, UCSD Description This page contains a collection of datasets that have been collected for research by our lab. Datasets contain the following features: user/item interactions star ratings timestamps product reviews social networks item-to-item relationships (e.g. copurchases, compatibility) product images price, brand, and category i
March 25, 2016 Tohru Iwasaki & Tetsuo Furukawa Department of Human Intelligence Systems Kyushu Institute of Technology This database contains a survey data of beverage preference from 604 respondents. The data represent the degrees of frequency drinking 14 beverages under 11 different situations. Thus the entire dataset is represented by a 3-dimensional array, i.e., a tensor of order 3. We collect
Amazon: Amazon Review Data includes reviews (ratings, text, helpfulness votes) and product metadata (descriptions, category information, price, brand, and image features), which includes a previous version in 2014 and an updated version in 2018. Our processed datasets are detailed here. Amazon 2014: This dataset contains product reviews and metadata from Amazon, including 24 categories and 142.8 m
gistfile1.md Movies Recommendation: MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73 Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/ Cornell University - Movie-review data for use in sentiment-
Donate!If you support the mission of the project, or rely on the data for your work, please consider making a donation to help make this effort sustainable. Quarterly data for the last year for each region is available for free download on this page. NEW! We now have regional archive files for research on entire countries: Australia, Canada, France, Germany, Greece, Italy, The Netherlands, Portuga
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