The music services that I subscribe to don’t understand me very well. Pandora, which puts together personalized radio stations, seems to think that I only like the very most popular music, which I don’t. Spotify, which offers a new personalized playlist for me each week, seems to think that I only like quite obscure music. But neither of them get it right, and I wish that I could tell them to change.
Wikipedia’s best content is mainly where its readers aren’t. For instance, the article about weddings is seen thousands of times every day, yet the community labels it “quite incomplete”, its prose “distinctly unencyclopedic”, and a call for additional sources to verify its content has been featured prominently at the top of the article for over four years. It turns out that this is not uncommon; each month Wikipedia’s articles are viewed billions of times, and over 40% of these views are to articles that would be of significantly higher quality if the encyclopaedia’s contributors followed their readers.
It has been about six years since we released our previous major ratings dataset, MovieLens 10M. Today, we have released its successor, MovieLens 20M, alongside two new non-archival datasets for education and development. These datasets are available for download at http://grouplens.org/datasets/movielens/. (more…)
Taavi proposed work on better explaining recommender systems output, focusing on the use of analogies to describe recommendations.
There were more than 16,000 applications for the Graduate Research Fellowship nationwide from all areas of science and engineering. The National Science Foundation (NSF) only awarded them to 2,000 students (of which GroupLens members were 0.1%!). The awards financially support 36 months of research and include opportunities for international research experiences.
Congratulations again, Taavi and Hannah!