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A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks Asela Gunawardana, Guy Shani; 10(100):2935−2962, 2009. Abstract Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the r
UCI Machine Learning Repository: Data Set
× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site. I'm sorry, the dataset "Netflix Prize" does not appear to exist. A note from the donor regarding Netflix data: "Thank you for your interest in the Netflix Prize dataset. The dataset is no longer available."
The 2009 GitHub Contest
Welcome to the 2009 GitHub Contest The 2009 GitHub contest is a simple recommendation engine contest. We have released a dataset of our watched repositories data and want to provide a list of recommended repositories for each of our users. Removed from the sample dataset are 4,788 watches - write an open source program to guess the highest percentage of those removed watches and you win our priz
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GitHub - livedoor/cicindela2: a highly customizable recommendation engine written in perl + MySQL
http://yowaken.dip.jp/tdiary/20070531.html
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