- 1 Recommender Systems
- 2 Wikipedia
- 3 Supporting Social Communication
- 4 Cyclopath
- 5 Participatory Sensing
- 6 Questions and Answers
- 7 Social Networking
- 8 Relational Crowdsourcing
GroupLens has a long history of research on recommender systems, starting with the original GroupLens USENET article recommender and the development of automatic collaborative filtering. That work continues today, as we run multiple recommendation services and use them to advance the art of recommendation.
MovieLens (http://movielens.org) 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.
LensKit is an open source toolkit for building, researching, and studying recommender systems. It is intended to support reproducible research on recommender systems, and provide recommendation technology for integration into research or production systems. More detailed information and documentation are available on the project page and GitHub.
The rating interfaces project is investigating different interfaces for rating movies. We are currently doing this by looking for interfaces that yield consistent ratings using re-rating experiments. We are also pursuing theoretic work that shows both how to measure ratings consistency, and explains why consistent ratings are probably the most efficient at gathering information about user preferences. Of course, we are also looking for rating interfaces that users will like.
Users annotate content online with tags – short, descriptive words or phrases. We are currently pursuing ideas organized around the idea of a tagging system as a garden: tag seeding, tag weeding, and tag architecture. For example, we are developing algorithmic techniques for identifying high-quality “seed” tags, and will be conducting user studies to determine which algorithms and interfaces have the best potential for increasing high-value tag applications across the system.
The BookLens project aims to be a book recommendation service. Similar to MovieLens, we hope that BookLens will help people find books to read. What makes BookLens different is that we aim to be a backend service for many different book communities. We currently are working with the Saint Paul Public Library as a recommender service and are looking to expand to libraries throughout the Twin Cities.
GroupLens has several ongoing research projects related to understanding and improving Wikipedia. We have employed data mining and analysis to better understand the differences in contribution value between Wikipedia contributors and insight into Wikipedia’s gender imbalance. We have also worked tounderstand the health of English Wikipedia’s user community and how it has changed over time.
SuggestBot (http://en.wikipedia.org/wiki/User:SuggestBot) does Intelligent Task Routing by matching Wikipedia articles in need of improvement with contributors who have shown interest in similar articles. It currently runs in four languages: English, Portuguese, Swedish, and Norwegian. While running as a service to the Wikipedia community it is also used as a platform for live experiments as we work on improving its performance.
This research focuses on understanding the differences in Wikipedia user behavior over time across different languages. We are working to understand the patterns of migration of users and content between different language versions of Wikipedia. We are also working to build predictive models of language proficiency.
Supporting Social Communication
We have a series of projects that focuses on the role of technology in supporting social communication and maintaining interpersonal relationships. We currently have three ongoing projects that relate to this domain, but we’re also open to initiating new investigations in this space! Please, reach out to Lana if you have questions or ideas.
Mediated Social Touch
Affectionate touch plays an important role in building and maintaining interpersonal relationships, frequently used to communicate friendliness, support, playfulness, and more. Just as video-mediated communication technologies like videochat allow users to stay in touch with friends and family through video and audio, mediated social touch is a set of technologies that allows people to touch across distance in order to reinforce social relationships. We’re currently building several prototypes using projector-camera systems and shape-shifting displays, but there is a lot of room to explore here with vibrotactile, thermal, and other haptic technologies.
Online Peer-Support in Recovery from Addiction
Online recovery communities offer free, easily-accessible peer support to people attempting to maintain abstinence from drugs and alcohol. These communities are an interesting Social Computing case study where both the priorities of privacy and the priority of engaging to form strong support networks are essential high-stakes part of everyday life and must be negotiated both at the individual and at the group levels. We are currently conducting a series of investigations that include developing both qualitative and quantitative understanding of current online practices and developing new mobile interventions. We’re open to new ideas in this space and particularly encourage students who have personal experience with recovery (e.g., self, family member, friend) to join the team.
Youth Video Practices
The goal of this project is understanding how children and teenagers use online video sharing platforms like YouTube and Vine to connect with friends and a broader audience. Knowing more about their current practices would help us to design interfaces that better support children’s creativity while keeping safety and privacy at the forefront. There are two current threads in this work. One is understanding current practices and designing supporting software. The other is developing general tools for understanding large video sets, particularly through crowdsourcing video analysis. Students interested in either thread are encouraged to join the team.
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.
Cycloplan is Cyclopath… for planners. We’ve added tools for traffic planners, engineers, and analysts, so they can analyze bicycle connectivity by creating and processing models. Planners can analyze how users currently are routed around the network, and they can model hypothetical infrastructure to determine how new (or even removed) links affect routing. Cycloplan is also access-controlled, so planners can create and maintain their own private copies of the map. And Cycloplan integrates with third-party GIS tools, making it easy to export and import data to and from the industry-standard Shapefile format, which allows users to edit data in their favorite third-party GIS applications.
The aim of this project is to make Cyclopath available to users on the go and to make use of location and sensing data on mobile devices to improve the map and the user experience. We are currently working on using GPS cycling tracks to improve map data (such as missing streets) and on using location to elicit context-aware user contributions such as landmarks. The Android app is available for download at Google Play.
Cyclopath in Greater MN
On a grant from the Minnesota Department of Transportation, GroupLens is expanding Cyclopath, which is currently limited to the 7-county metro area around Minneapolis and St. Paul, to the entire state of Minnesota. Currently in progress, this project has three main aspects: (a) Merging all the state’s road and trail data and connecting it appropriately with the existing metro area data within Cyclopath; (b) Scaling and tuning the route-planner algorithm so that it is able to serve long routes (such as from Rochester to International Falls) in quick time; and (c) Improving the user interface (the Flash client that runs within the browser) to make it easier for new users to get adjusted to and start using and contributing to Cyclopath. The Cyclopath team collaborates chiefly with Greta Alquist from MN/DoT for this project.
We are developing and studying platforms that support citizen science, mobile crowdsourcing, and volunteered geographic information. These projects largely are concerned with processing the submissions of simple geographic data (e.g., GPS locations or photos) by on-location volunteers from mobile devices.
FolkSource is a platform targeted at groups such as local municipalities or non-profits. It supports a wide array of civic-minded public engagement opportunities by using the sensors in smartphones (e.g., GPS, camera, microphone) to crowdsource location-specific data. Organizations define campaigns, or data collection goals, that anyone with a smartphone can participate in. Technologically, we are preparing to launch our this system. We’re interested in a number of research areas within this system, such as motivating participation, mechanisms for ensuring or judging data quality, and understanding campaign organization workflows. As an early trial, we’ve collaborated with the Humphrey School of Public Affairs to build a bicycle and pedestrian traffic counting campaign. Current work is pursuing the use of mobile crowds to gather and communicate space-usage rules in online or mobile maps.
Questions and Answers
Past research in this area focused on understanding patterns of behavior and content generation in online communities such as Yahoo! Answers, Stack Overflow and Turbo Tax. We have learned a great deal about the potential of these sites in helping people, ways to identify types of questions and users, nurture experts, improve contributions, and more. In order to study these systems at the core level, we have built a campus-wide question and answer service to support online field studies. We expect this research infrastructure will allow us to address research questions relating to intelligent task routing and question recommendation algorithms and interfaces.
We are studying the behavior of users in social networking sites like Twitter and Pinterest with the goals of better understanding the pattern of interactions and developing improved methods for personalization. For example, we have developed methods for distinguishing informational and conversational messages in Twitter, and we have performed a statistical study of Pinterest to understand who participates and what types of content are shared.
Many online information resources rely on their users’ contribution. This project focuses on the question of how to turn information consumers into contributors. Specifically, we seek to experimentally develop an integrative theory of relational crowdsourcing — a term we use to describe the process of turning a consumer into a long-term contributor through a series of requests, feedback, and interactions. This theory models efforts to invite consumers to contribute, integrating properties of user history and four key parameters of those efforts: (1) when to make a request of the user, (2) what task (or task type) to request of the user, (3) how to make that request (including the nature of the appeal), and (4) what type of feedback to offer, including expressions of gratitude and impact of the user contribution.