Jester is a joke recommender system developed at UC Berkeley to study social information filtering.
Version 5.0, launched on April 1, 2015, includes new jokes and algorithms, and a redesigned interface.
Datasets containing over 5 million anonymous joke ratings from 150k users are freely available for research use at: http://eigentaste.berkeley.edu/dataset/
As a courtesy, if you use the data, we would appreciate knowing your name, what research group you are in, and the publications that may result.
Eigentaste: A Constant Time Collaborative Filtering Algorithm [bibtex] Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Information Retrieval, 4(2), 133-151. July 2001.
Eigentaste 5.0: Constant-Time Adaptability in a Recommender System Using Item Clustering [bibtex] Tavi Nathanson, Ephrat Bitton, and Ken Goldberg. ACM Conference on Recommender Systems (RecSys), Minneapolis, MN, Oct 2007.
Algorithms, Models and Systems for Eigentaste-Based Collaborative Filtering and Visualization [bibtex] Tavi Nathanson. Master's thesis, EECS Department, University of California, Berkeley, May 2009.
Jay Patel, Viraj Mahesh, Sanjay Krishnan
EECS, UC Berkeley
Professor, IEOR and EECS, UC Berkeley