We publish research articles in conferences and journals primarily in the field of computer science, but also in other fields including psychology, sociology, and medicine. See our publications page for a full list; see below for excerpts from recent articles.
Content Production and Reader Interest in Wikipedia
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. Read more.
Onboarding New Users in Recommender Systems
According to the song, “Getting to know you…. getting to know all about you…” is a lot of fun. But when you go to a new doctor, and fill out a 10-page patient intake form so the doctor can get to know you, it’s not so much fun.
The same has been true for recommender systems. A typical experience for new users is to rate a bunch of items to let the system know their preferences. For example, in the MovieLens film recommender, first-time visitors had to rate 15 movies (see the screen below). This process usually required paging through multiple screens, took over 5 minutes, and discouraged some people so much they dropped out before ever making it to the site home page!
MovieLens is a web site that helps people find movies to watch. It has hundreds of thousands of registered users. We conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders and interfaces, member-maintained databases, and intelligent user interface design.
Find bike routes that match the way you ride. Share your cycling knowledge with the community. Cyclopath is a geowiki: an editable map where anyone can share notes about roads and trails, enter tags about special locations, and fix map problems – like missing trails. Hundreds of Twin Cities cyclists are already doing this, making Cyclopath the most comprehensive and up-to-date bicycle information resource in the world.
LensKit is an open source toolkit for building, researching, and studying recommender systems. Do you need a recommender for your next project? LensKit provides high-quality implementations of well-regarded collaborative filtering algorithms and is designed for integration into web applications and other similarly complex environments.