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Consumers are increasingly using the web to find answers to their health‐related queries. Unfortunately, they often struggle with formulating the questions, further compounded by the burden of having to traverse long documents returned by the search engine to look for reliable answers. To ease these burdens for users, automated consumer health question answering systems try to simulate a human professional by refining the queries and giving the most pertinent answers. This article surveys state‐of‐the‐art approaches, resources, and evaluation methods used for automatic consumer health question answering. We summarize the main achievements in the research community and industry, discuss their strengths and limitations, and finally come up with recommendations to further improve these systems in terms of quality, engagement, and human‐likeness. [ABSTRACT FROM AUTHOR]

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being published on the topic each year, one might think the recommendation problem is almost solved. In reality, however, the largemajority of published works focuses on algorithmic improvements and relies on data-based evaluation procedures which may sometimes tell us little regarding the effects new algorithms will have in practice. This special issue contains a set of papers which address some of the open challenges and frontiers in RSs research: (i) building interactive and conversational solutions, (ii) understanding recommender systems as socio-technical systems with longitudinal dynamics, (iii) avoiding abstraction traps, and (iv) finding better ways of assessing the impact and value of recommender systems without field tests. [ABSTRACT FROM AUTHOR]

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender system requires more than a clever general-purpose algorithm. It requires an indepth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the forefront. It features articles on the latest learning technology, reflects on the humancomputer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology. [ABSTRACT FROM AUTHOR]

Personalizing behavior change (BC) strategies to motivate increased physical activity is especially important for the diverse older adult population. However, there is a lack of knowledge about how to profile older users to most effectively personalize BC solutions. Self-awareness and social awareness are BC strategies commonly used in commercially available applications to promote physical activities. Through a randomized controlled trial (N = 53), we studied the effect of some personal factors on the physical activity of older adults under these two strategies. For this purpose, each BC strategy was implemented in a mobile application. Based on the statistical analysis of the measured step data and the collected questionnaire data, we identified a list of personal factors to personalize each BC strategy towards improved physical activity. Hereby we suggest how to create effective motivational profiles and provide design recommendations to personalize these BC strategies toward increased physical activity for older users. [ABSTRACT FROM AUTHOR]

This research was motivated by a desire to help office workers change their sedentary behavior because a prolonged sedentary posture increases the risks of developing musculoskeletal injuries and chronic diseases, thus threatening their physical and psychological well-being. Regular breaks involving low-effort physical activities are effective in reducing the adverse impacts of inactive behaviors. In this article, we present the design of a posture-based interactive system called HealthSit, which was developed to promote a short lower-back stretching exercise during work breaks. Through a within-subject study involving 30 office workers, the effectiveness of HealthSit in facilitating the stretching exercise was examined by making comparisons between an interaction-aided, a guided, and a self-directed exercise mode. We also used HealthSit as a research probe to investigate the interactivity of the system in enhancing user experience and the psychological benefits of the fitness breaks. Compared with the other two modes, the interaction-aided exercise mode significantly improved the quality of the stretching exercise and enhanced motivation and emotional state. These results confirm the effectiveness of HealthSit in supporting fitness breaks as a new workplace technology. Based on our study, a set of design implications have been derived for technology-assisted fitness work breaks. [ABSTRACT FROM AUTHOR]

An important task for intelligent healthcare systems is to predict the effect of a new intervention on individuals. This is especially true for medical treatments. For example, consider patients who do not respond well to a new drug or have adversary reactions. Predicting the likelihood of positive or negative response before trying the drug on the patient can potentially save his or her life. We are therefore interested in identifying distinctive subpopulations that respond differently to a given intervention. For this purpose, we have developed a novel technique, Intervention-based Clustering, based on a Bayesian mixture model. Compared to the baseline techniques, the novelty of our approach lies in its ability to model complex decision boundaries by using soft clustering, thus predicting the effect for individuals more accurately. It can also incorporate prior knowledge, making the method useful even for smaller datasets. We demonstrate how our method works by applying it to both simulated and real data. Results of our evaluation show that our model has strong predictive power and is capable of producing high-quality clusters compared to the baseline methods. [ABSTRACT FROM AUTHOR]

Recommender systems have increasingly become popular in various web environments (such as e-commerce and social media) for automatically generating items that match to individual users' personal interests. Among different types of recommender systems that have been developed so far, critiquing-based recommender systems have been widely recognized as an effective approach to obtaining users' feedback on the system's generated recommendations. Such systems have been demonstrated particularly helpful for serving new users. That is, by means of eliciting and refining their preferences through real-item feedback, the system is able to gradually improve its recommendation accuracy and aid users to make better decision. However, how to precisely acquire users' critiquing feedback is still a challenging issue. Most of existing systems rely on users to specify the feedback on their own, which unavoidably let users consume extra efforts. In our work, we have been engaged in analyzing users' eye-movement behavior when they evaluate recommendations, with the objective of identifying the correlation between eye movements and their critiquing feedback. The results can hence be constructive for developing an eye-based feedback elicitation method, so as to reduce users' self-critiquing efforts. Based on a collection of real users' eyegaze data, we have tested this idea's feasibility. Moreover, we have compared different recommendation interfaces (the interface that displays a set of recommended products), and found the category layout performs better than the list structure in terms of stimulating users to view recommended products. As a result, multiple design guidelines are derived from our user experiment. [ABSTRACT FROM AUTHOR]

Diabetic and obese users given a Fitbit fitness tracker for seven months identified the device's playfulness, practicality, persuasiveness, personalization, and privacy as the top motivators for its use. These results provide insights into designing wearable health monitors that are engaging enough to effect long-term change. In the web extra at https://youtu.be/ByQNA06-pS8, guest editor Katarzyna Wac interviews lead author Mirana Randriambelonoro, a PhD candidate in information systems at the University of Geneva. [ABSTRACT FROM AUTHOR]