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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.

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 large majority 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.

The paper presents a qualitative study to explore the use of fitness trackers and their social functions in intergenerational settings. The study covered three phases of semi-structured interviews with older and younger adults during individual and intergenerational use of the fitness trackers. The study revealed comparability as common fitness practice for older adults. The findings show that intergenerational fitness tracking practices can increase in-person meetings and daily discourses and thus enhance family social bonds. An unexpected benefit of this practice is its ability to help older adults overcome technology barriers related to the use of fitness trackers. Overall speaking, families whose intergenerational members already enjoy a strong relationship are likely to gain the most from such practices. Many challenges remain especially concerning the motivation and involvement of younger partners and the user experience design aspect of such digital programs. For this purpose, we have developed some recommendations for the future development and deployment of intergenerational fitness tracking systems to stimulate interactions between younger and older family members and thus to promote their physical and emotional well-being.

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 in‐depth 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 human‐computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

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.

Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on costly manual annotation. To alleviate this burden, we propose a distant supervision method—Dystemo—for automatically producing emotion classifiers from tweets labeled using existing or easy-to-produce emotion lexicons. The goal is to obtain emotion classifiers that work more accurately for specific applications than available emotion lexicons. The success of this method depends mainly on a novel classifier—Balanced Weighted Voting (BWV)—designed to overcome the imbalance in emotion distribution in the initial dataset, and on novel heuristics for detecting neutral tweets. We demonstrate how Dystemo works using Twitter data about sports events, a fine-grained 20-category emotion model, and three different initial emotion lexicons. Through a series of carefully designed experiments, we confirm that Dystemo is effective both in extending initial emotion lexicons of small coverage to find correctly more emotional tweets and in correcting emotion lexicons of low accuracy to perform more accurately.

The cost of healthcare is expected to grow enormously in the coming years. To keep these costs limited, we need better technological tools for self-monitoring and independent aging and to put a stronger emphasis on wellness, defined as a balanced and healthy lifestyle that avoids diseases at every stage of life. In contrast to traditional medical science that examines patients in laboratories, wellness has so far been difficult to measure objectively. This situation is changing due to the availability of wearable and pervasive sensors, smartphones, and other body-worn devices that can track and infer users’ activities. Technological advances in this area are rapidly pushing forward. The range of methods as well as the health domains where sensors have been used for monitoring is proliferating. This has created a unique opportunity to understand wellness in an integrative and balanced framework. The goal of this survey is to summarize the latest innovations in sensor technology and present the state-of-the-art of personal wellness management systems with the purpose of shedding light on where this field is developing and where the future research opportunities lie.

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As suchsystems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of this article. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about trade‐offs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state of the art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

Consumer decision support systems (CDSSs) help online users make purchasing decisions in e-commerce Web sites. To more effectively compare the usefulness of the various functionalities and interface features of such systems, we have developed a simulation environment for decision tasks of any scale and structure. Furthermore, we have identified three criteria in an evaluation framework for assessing the quality of such CDSSs: users’ cognitive effort, preference expression effort, and decision accuracy. A set of experiments carried out in such simulation environments showed that most CDSSs employed in current e-commerce Web sites are suboptimal. On the other hand, a hybrid decision strategy based on four existing ones was found to be more effective. The interface improvements based on the new strategy correspond to some of the advanced tools already developed in the research field. This result is therefore consistent with our earlier work on evaluating CDSSs with real users. That is, some advanced tools do produce more accurate decisions while requiring a comparable amount of user effort. However, the simulation environment will enable us to efficiently compare more advanced tools among themselves, and indicate further opportunities for functionality and interface improvements.

We consider constructivepreference elicitation for decision aid systems in applications such as configuration or electronic catalogs. We are particularly interested in supporting decision tradeoff, where preferences are revised in response to the available outcomes. In several user-involved decision aid systems we designed in the past, we were able to observe three generic tradeoff strategies that people like to use. We show how a preference model based on soft constraints is well-suited for supporting these strategies. Such a framework provides an agile preference modelparticularly powerful for preference revision during tradeoff analysis. We further show how to integrate the constraint-based preference model with an interaction model called example-critiquing. We report on user studies which show that this model offers significant advantages over the commonly used ranked-list model, especially when the decision problem becomes complex.

Many information systems are used in a problem solving context. Examples are travel planning systems, catalogs in electronic commerce, or agenda planning systems. They can be made more useful by integrating problem-solving capabilities into the information systems. This poses the challenge of scaleability: when hundreds of users access a server at the same time, it is important to avoid excessive computational load. We present the concept of SmartClients: lightweight problem-solving agents based on constraint satisfaction which can carry out the computation- and communication-intensive tasks on the user's computer. We present an example of an air travel planning system based on this technology.

The frequent use of past experience by human engineers when solving new problems has led to an interest in the use of case based reasoning (CBR) to help automate engineering design. In engineering design it often occurs that many past exp eriences must be combined to solve a new problem, and thus the process of case based adaptation must efficiently and systematically combine information from many sources. We have developed a constraint based methodology for case combination that allows its application across a wide range of problems. We have shown that our approach provides an efficient adaptation methodology that ensures convergence upon a solution if one exists, provides a uniform representation of cases, and is generalizable beyond just one domain. Our technique is implemented in a case based reasoning system called COMPOSER, which ha s been tested in two design domains: assembly sequence design and configuration design.