A Uncertainty Perspective on Qualitative Preference

Collaborative filtering has been successfully applied for predicting a person\u27s preference on an item, by aggregating community preference on the item. Typically, collaborative filtering systems are based on based on quantitative preference modeling, which requires users to express their preferences in absolute numerical ratings. However, quantitative user ratings are known to be biased and inconsistent and also significantly more burdensome to the user than the alternative qualitative preference modeling, requiring only to specify relative preferences between the item pair. More specifically, we identify three main components of collaborative filtering-- preference representation, aggregation, and similarity computation, and view each component from a qualitative perspective. From this perspective, we build a framework, which collects only qualitative feedbacks from users. Our rating-oblivious framework was empirically validated to have comparable prediction accuracies to an (impractical) upper bound accuracy obtained by collaborative filtering system using ratings

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112

Development and validation of a diabetes-specific health state classification system and valuation function based on the multi-attribute theory

Preference-Based Measures of Health (PBMH) provide \u27preference\u27 or \u27utility\u27 weights that enable the calculation of quality-adjusted life years for the economic evaluations of interventions. The Diabetes Utility Index (DUI) was developed as a two-page, self-administered diabetes-specific PBMH that can replace expensive time-consuming interviews with patients to estimate their health state utilities. Inputs from theory, an existing diabetes-specific measure of quality of life, and statistical analyses were submitted to a clinical expert panel. After three rounds of pilot surveys (n1=52, n2=65, n3=111) at primary care clinics in Morgantown, WV, five attributes and severity categories for each attribute were finalized on the basis of the results of Rasch Analysis and consultations with the panel. The final attributes were: \u27Physical Ability & Energy\u27, \u27Relationships\u27, \u27Mood & Feelings\u27, \u27Enjoyment of Diet\u27, and \u27Satisfaction with Management of diabetes\u27. The next step involved obtaining preferences for health states based on combinations of DUI attributes and severity levels from 100 individuals with diabetes, recruited from primary care and community settings in and around Morgantown, WV, in hour-long one-on-one interviews. These health states were anchor states, single-attribute level states including corner states, and marker states. The interviews provided data to calculate a Multi-Attribute Utility Function (MAUF) that calculates utilities for any of the 768 health states that can be defined by the DUI, on a scale where 1.00=Perfect Health and 0.00=the all worse \u27Pits\u27 state, from respondents\u27 answers to its five questions. In addition to an overall index score, attribute-level preference scores were also calculable by the function. Finally, a validation survey was conducted in collaboration with the West Virginia University (WVU) Diabetes Institute. For concurrent and construct validation purposes, the DUI was mailed to individuals with diabetes along with generic PBMH like the EuroQol EQ-5D, the SF-6D and other patient-reported outcomes measures like the Diabetes Symptoms Checklist-Revised, the Short Form 12 (SF-12) and the Well-Being Questionnaire (W-BQ12), and their surveys responses (n=396) were merged with a clinical database consisting of ICD-9 diagnosis codes. The DUI utilities were found to be largely free of socio-demographic effects and its scores were well distributed between 0.00 and 1.00. The DUI moderately correlated with generic PBMH and distinguished between severity groups based on diabetes symptoms and complications. The scoring function of the DUI calculated utilities favorably compared against cardinal Standard Gamble utilities obtained directly from patients for three DUI health states. These results show evidence of the feasibility and validity of the DUI. Further research is suggested to demonstrate the generalizability of these findings, to study the responsiveness of the DUI, and to examine the clinical meaningfulness of the DUI change scores

Direct and indirect effects of mood on risk decision making in safety-critical workers

The study aimed to examine the direct influence of specific moods (fatigue, anxiety, happiness) on risk in safety-critical decision making. It further aimed to explore indirect effects, specifically, the potential mediating effects of information processing assessed using a goodness-of-simulation task. Trait fatigue and anxiety were associated with an increase in risk taking on the Safety-Critical Personal Risk Inventory (S-CPRI), however the effect of fatigue was partialled out by anxiety. Trait happiness, in contrast was related to less risky decision making. Findings concerning the ability to simulate suggest that better simulators made less risky decisions. Anxious workers were generally less able to simulate. It is suggested that in this safety-critical environment happiness had a direct effect on risk decision making while the effect of trait anxiety was mediated by goodness-of-simulation

A Transdisciplinary Approach to Decision Support for Dams in the Northeastern U.S. with Hydropower Potential

The Federal Energy Regulatory Commission (FERC) is the regulatory body that oversees non-federally owned dam operations in the United States. With more than 300 hydropower dams across the U.S. seeking FERC relicense between 2020 and 2029, and 135 of those dams within the Northeast region alone, it is prudent to anticipate and plan for such decision-making processes. Anyone may be involved in FERC relicensing; in fact, FERC solicits public comment and requires the licensee to hold a public hearing during the process. Parties may also elect to apply for legal intervenor status, allowing them a more formal entry into the relicensing process. However, there are two key barriers that may keep the public from participating in a dam decision-making process in an impactful way. The first of these barriers is access to information. Having access to the types of information that matters to FERC is important, because it allows the participant to communicate their support or concerns about the relicensing using the language of the process. In particular, participants other than the licensee may not have access to project economic information, so this is a focus in my research. The second barrier is capacity to participate in a way that impacts the process (i.e., institutional knowledge about what kinds of decision criteria (factors) and decision alternatives (project options), as well as relevant data, that FERC typically weighs in their decision making or has considered in the past). Actors not privy to license information (perhaps encountering difficulty in navigating the FERC eLibrary), lacking knowledge of FERC process conventions, or otherwise unfamiliar with hydropower dam schemes or operations have substantial hurdles preventing their effective participation. My research, situated in the sustainability science arena, addresses hydropower project cost and performance assessment and multi-criteria considerations for dam decision support. I lead the development and assessment of an online Dam Decision Support Tool aimed at addressing barriers to the hydropower dam decision-making process. My work demonstrates possibilities for tailoring decision tools to incorporate stakeholder perspectives into decision making about hydropower dams