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 blog for research highlights and our publications page for a comprehensive view of our research contributions. Here are excerpts from recent articles:
Re-thinking Top-N recommendation lists: should recommenders always show the best content?
Recommender systems typically are optimized to produce a top-N list reflective of the most-highly recommended items a user has not yet rated. However, there are many reasons to believe that this order may not be the best order to present items to users, either within or across sessions.
Presenting the Wikidata Human Gender Indicators
For many years Wikipedia’s editor gender gap has been widely discussed, but its content gender gap has received less attention. This summer we presented our work in developing WIkidata Human Gender Indicators (WHGI) at OpenSym ‘16 which provides statistical insight into the composition of Wikipedia biographies through the use of Wikidata. WHGI has allowed us to research details about the character of the biography gender gap—that it is increasingly looking like the political biases of the real world—and to arm community editing groups with metrics about their work. For instance we are providing the data that allows Wikiproject Women in Red to reflect that, “[…] in November 2014, just over 15% of the English Wikipedia’s biographies were about women. Since then, we have improved the situation slightly, bringing the figure up to 16.52%, as of 9 October 2016. But that means, according to WHGI, only 232,357 of our 1,406,482 biographies are about women.”
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.