HealthyTogether: Exploring Social Incentives for Mobile Fitness Applications

Title: HealthyTogether: Exploring Social Incentives for Mobile Fitness Applications
Dates: 2012 – present
Researchers: Yu Chen and Pearl Pu
Keywords: Mobile fitness applications; gamification; competition; cooperation, group interaction.
Abstract: A crucial element in many mobile fitness applications is gamification that makes physical activities fun. While many methods focus on competition and individual users’ interaction with the game, the aspect of social interaction and how users play games together in a group remains an open subject. To investigate these issues, we developed a mobile game, HealthyTogether, to understand how users interact in different group gamification settings: competition, cooperation, or hybrid. We describe the results of a user study involving 18 dyads (N=36) over a period of two weeks. Results show that users significantly enhanced physical activities using HealthyTogether compared with when they exercised alone by up to 15%. Among the group settings, cooperation (21% increase) and hybrid (18% increase) outperformed competition (8% increase). Additionally, users sent significantly more messages in cooperation setting than hybrid and competition. Furthermore, physical activities are positively correlated with the number of messages they exchanged.
Sponsor: Swiss National Science Foundation
Links: Main Page

Recognizing Emotions in Olympic Tweets

Title: Fine-Grained Emotion Analysis in the Tweets about Olympic Games
Dates: 2012-current
Researchers: Valentina Sintsova and Pearl Pu
Keywords: Emotion Recognition, Social Media Analysis, Emotion Visualization, Human Computation
Description: Social media platforms such as Twitter or Sina Weibo have become a common way for people to share opinions and emotions. Sports events are traditionally accompanied by strong emotions and the 2012 summer Olympic Games in London were not an exception. This project aims to develop tools for analysis of the emotions expressed in social media during the Olympic Games or other popular sports events. We use the 20 fine-grained emotion categories of GEW to allow users to distinguish the personal reactions with more details. We work both on the automatic extraction of those emotions in the tweets and on their compact visualization.
Sponsor: Swiss National Science Foundation
Links: Main Page (images, visualization demo and video, AMT task demo, emotion recognition poster and paper)


Title: Empatheticons: Designing Emotion Awareness Tools for Group Music Experience
Year: 2012
Researchers: Yu Chen and Pearl Pu
Keywords: Group music player, user experience, social interaction, group influence, emotion awareness, emotion presentation
Description: Online group music players provide music playlists for a group of people. In such systems, group members sometimes listen to group songs individually. How to create the sense of connectedness for members who are listening to music in different time and locations is understudied. In this project, we investigate the roles of emotion awareness tools and how they may enable connectedness. We first describe the design of empatheticons, a set of kinetic emotion representations. We then show that they allow users to represent, annotate, and visualize group members’ emotions in GroupFun, a group music player. An in-depth user study (N = 18) with GroupFun demonstrates that empatheticons enhance users’ perceptions of the connectedness (immediacy) and familiarity (intimacy) with each other in a non-collocated and asynchronous setting. Furthermore, users’ emotion annotation for group songs can be influenced by other group members. We conclude with design implications and directions for future research.
Sponsor: Swiss National Science Foundation
Links: main page (images, video, demo, paper)


Title: ResQue: A User-Centric Evaluation Framework for Recommender  Systems
Dates: 2010 – 2011
Researchers: Rong Hu, Li Chen and Pearl Pu
Keywords: Recommender systems, quality of user experience, e-Commerce recommender, post-study questionnaire.

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users’ point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems’ Quality of user experience), which aimed at measuring the qualities of the recommended items, the system’s usability, usefulness, interface and interaction qualities, users’ satisfaction with the systems, and the influence of these qualities on users’ behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.

Sponsor: Swiss National Science Foundation
Links: Main page


Title: Designing emotion annotation interface for group recommender systems
Dates: 2011
Researchers: Yu Chen and Pearl Pu
Keywords: Emotion awareness; emotion annotation; group recommender systems; group influence 
Abstract: Group recommender systems help users to find items of interest collaboratively. Support for such collaboration has been mainly provided by interfaces that visualize membership awareness, preference awareness and decision awareness. However, these mechanisms do not address group influence issues: how members may affect each other. In this paper, we investigate the roles of emotion awareness interfaces and how they may enable positive group influence. We first describe the design process behind an emotion annotation tool, which we call CoFeel. We then show that it allows users to annotate and visualize group members’ emotions in GroupFun, a group music recommender. An in-depth pilot user study with GroupFun suggests that emotion awareness features have the potential to enable group influence on preferences among group members. Most importantly, users reported CoFeel as a promising tool to annotate group recommended items in terms of accuracy and engagement. Finally, we provide design implications for emotion awareness interfaces in group recommender systems.
Sponsor: Swiss National Science Foundation
Links: Main Page


Title: Group recommender systems as a voting problem
Year: 2010-2012
Researchers: George Popescu and Pearl Pu
Keywords: Voting, Recommender systems, Group decision making, Social choice, Social websites, Dynamic selection, Preference aggregation, User incentives, Game Theory, Music preference, online user decision and behavior, social websites
Abstract: Aggregating users’ individual preference and recommending a common set of items for a group has become a challenging topic in group recommender systems and social websites. This issue is mainly concerned with the following three objectives: eliciting individual users’ preferences, suggesting outcomes that maximize the overall satisfaction for all users and ensuring that the aggregation mechanism is resistant to individual users’ manipulation. Firstly we show how our proposed probabilistic weighted-sum algorithm (PWS) works and emphasize on its advantages. Then we compare PWS with related approaches implemented in similar systems using the case of our music recommender, GroupFun. We describe an experiment design to study users’ perceptions of the algorithms, their perceived fairness and incentives to manipulate the final recommendation outcome. We expect our results to show that PWS will be perceived as fair and diversity- and discovery-driven, thus enhancing the group’s satisfaction. Our future work will focus on the actual evaluation of GroupFun using the experiment design presented here.
Sponsor: Swiss National Science Foundation
Links: Main page, Application

An Information Processing Model and Interaction Principles for Decision Tradeoff

Title: An Information Processing Model and Interaction Principles for Decision Tradeoff
Dates: 2004 – 2008
Researchers: Jiyong Zhang and Pearl Pu
Keywords: information processing model, interaction principles, tradeoff
Abstract: CritiqueShop is an online platform for designing and evaluating e-commerce product search tools based on the critiquing technique. It provides a unified user interface so that the performances of different recommendation algorithms can be evaluated under the same condition. This system is developed with Java/AJAX (see Google web toolkit for the detail of this technology). For more information about this system, please see our publications. Click here for more.
Sponsor: Swiss National Science Foundation
Links: Main pageApplication, image gallery

Preference Elicitation

Title: Preference Elicitation
Dates: 2003 – 2008
Researchers: Li Chen and Pearl Pu
Keywords: preference model, users’ beliefs, cognitive and emotional limitations
Abstract: As people increasingly rely on interactive decision support systems to choose products and make decisions about tradeoffs, we must understand how interface technologies influence user behaviors. Crucial to this issue is to develop the mathematical decision theory into a new theory of decision information processing model, which shows how to present the most meaningful information in relation to users’ task goals and supporting context, and how to adaptively change the information content when goals evolve. Building effective interfaces for interactive decision support systems is challenging because 1) users’ preference models are incomplete and it is hard to elicit value functions for preferences that do not exist; 2) users’ beliefs about desirability are ephemeral, uncertain and context dependent; and 3) users have cognitive and emotional limitations for processing information. To develop a useful theory of decision information processing, we need to further understand the process by which humans make tradeoff decisions, how information affects this process, and how to construct effective interface technologies to augment user performance.
Sponsor: Swiss National Science Foundation
Links: Home page

Interaction Models for Preference Elicitation

Title: Interaction Models for Preference Elicitation
Researchers: Pratyush Kumar and Pearl Pu
Keywords: focus + context, information visualization
Abstract: Human centered decision making principles have been the center of massive research activities in the recent past. While on one hand traditional decision theory claims to predict exact solution for a decision problem, provided a value function is defined. A value function is one which can predict accurately about decision values given precise value of the attributes. Researchers have long found the problem of defining a precise value function tough in real life scenarios. The problem becomes even more complicated when the decision task is a multi attribute function. A simple example of such a task is the selection of a multi attribute product. Our work centers around selection of a multi attribute product on an e-store. Many tools are currently available which help users in making the crucial decision. The major impediment with such tools is the fact that under uncertainty in preferences, they are only as good in helping the users in their decision task, as users themselves are in their preferences. Experiments done with users have suggested the fact that often preferences with users are fluid and uncertain, hence a proper method has to be designed to elicit those unformed and often unspecified preferences. It is these preferences that lead to a definite decision goal. Under the scenario of uncertain and unspecified preferences, the task of defining a precise value function also becomes highly complex and difficult. We believe that such hidden and unstated preferences can be elicited by providing users with example. This process is called example critiquing. The users are shown examples of the products they are looking for, and then they discover their preferences when attributes of the example violated any of their hidden preferences. The idea is that users do not know their preferences till they see them being violated. Thus, we believe that such example critiquing can be crucial in eliciting preferences of users and help them in better decision making. 
Sponsor: Swiss National Science Foundation
Links: Main page; Demo 1: RankedListUI; Demo 2: TweakingListUI