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:
87% of People Got This Question about Their Door Lock Wrong!
“You drive home and park. Your car is full of groceries and other shopping, which take many trips to bring into the house. Five minutes after you drove in, you are still making trips to the car. Is the door locked or unlocked?” What if I told you that 87% of people got this question wrong? Sensors and “smart” devices for your home may hold the promise of making life more convenient, but they may also make it harder to understand and predict things like the state of you “smart” door lock in common situations like the one above. Want to give it a try?
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.
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.