2,373 research outputs found
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
Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review
Background: Recommender systems are information retrieval systems that provide users with relevant items
(e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in
healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing
the cost of healthcare and fostering a healthier lifestyle in the population.
Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature
published over the past 10 years on the use of health recommender systems for patient interventions. The aim of
this study is to understand the scientific evidence generated about health recommender systems, to identify any
gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy
lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to
propose some solutions.
Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health
recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing
Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal
articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results
simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each
paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion
theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary
taxonomy.
Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three
features were assessed. The nine features associated with the health promotion theoretical factors and behavior
change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not
assess (cost)-effectiveness.
Discussion: Health recommender systems may be further improved by using relevant behavior change strategies
and by implementing essential characteristics of tailored interventions. In addition, many of the features required
to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects
were not reported in the studies.
Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender
systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should
ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with
electronic health records and the incorporation of health promotion theoretical factors and behavior change
theories. This will render those studies more useful for policymakers since they will cover all aspects needed to
determine their impact toward meeting SDG3.European Union's Horizon 2020 No 68112
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects
In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare
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The influence of national culture on the attitude towards mobile recommender systems
This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation
of Korea Grant funded by the Korean governmen
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