Work-related musculoskeletal disorders, often caused by unsafe lifting techniques, remain a persistent threat to worker health and safety. We present SAFELIFT, a safety-aware recommender system that automatically detects risky lifting behaviors and generates corrective feedback. Using monocular video input, SAFELIFT extracts ergonomic parameters to compute the Lifting Index (LI) from the Revised NIOSH Lifting Equation. When the LI exceeds a safety threshold, the system produces both graphical and textual recommendations to promote safer postural strategies. Unlike prior approaches, SAFELIFT requires no wearable sensors or multi-camera setups, enabling scalable and low-cost deployment in workplace environments. To assess its effectiveness, we conducted a two-phase evaluation: (1) domain experts (ergonomists, occupational safety professionals, medical staff) assessed the accuracy and relevance of the recommendations, and (2) lay users evaluated different presentation formats, judging their clarity, helpfulness, and trustworthiness. By integrating ergonomics with recommender system design, SAFELIFT contributes to a new class of context-aware, safety-oriented recommendation technologies for occupational health.

Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety. This paper presents a health recommender system designed to prevent injury by assessing and correcting posture for lifting techniques. Leveraging monocular video input, our method estimates key ergonomic parameters to compute the Lifting Index based on the Revised NIOSH Lifting Equation. When the computed Lifting Index exceeds a predefined safety threshold, the system automatically generates graphical and textual recommendations to guide the worker towards safer postural strategies. This safety-aware recommender system provides interpretable and actionable feedback without requiring wearable sensors or multi-camera setups, making it suitable for deployment in real-world workplace environments. By integrating ergonomics with recommender system design, we contribute to a new class of context-aware, safety-oriented recommendation technologies tailored for occupational health.

Launched in 2016, the Health Recommender Systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors. Despite the unforeseen pause due to the COVID-19 pandemic and other challenges, the workshop’s influence persisted through its vibrant community and publications. Our aim with the 6th HealthRecSys is to reignite these conversations and provide a forward-thinking platform that revisits the foundational elements that have contributed to the field’s growth. However, the workshop aspires to do more by infusing new perspectives and tackling the most pressing global challenges and technological innovations head-on with contemporary themes such as the impact of global health crises, generative AI models, personalized and self-managed care, and the increasing focus on health equity. HealthRecSys is dedicated to strengthening the network of researchers working on health recommender systems, drawing participants from an array of health and care domains. Through our combined interactive and paper based workshop format, we aim at cultivating a cross-disciplinary community that promotes collaboration among recommender systems specialists, healthcare professionals, ethicists, and policymakers, among others.

Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item is a task or behavior change activity, several variables, such as the difficulty of the task and users’ ability to achieve it, in addition to user preferences and needs, determine the suitability of the recommendations. This paper focuses on how user ability and task difficulty concepts can be integrated into the recommendation process to personalize health activity recommendations. To this end, we compare five approaches, some borrowed from the sports and gaming world, and explore their application, advantages, and drawbacks. Through a study of two weeks, we obtained a suitable dataset to investigate how these algorithms can be used for a health recommender system (HRS) and which one is the most appropriate choice for an online HRS in terms of characteristics and flexibility required for behavior change related tailoring. We compared this choice with a baseline algorithm as part of a fully functional HRS to assess the feasibility and impact of integrating the user ability and required effort concepts on the user engagement with the recommendations in an online longitudinal study of two weeks. The results overall suggest that such integration is effective, and in addition to realizing health behavior change requirements, it improves user engagement with the recommendations.

Supporting personal health with Decision Support Systems (DSS) and, specifically, recommender systems (RS) is a promising and growing area of research. Integrating the user in the loop is vital in such health systems due to the complexity of recommendations, gravity of the decisions and the reliance on user autonomy. However, for such a purpose, to the best of our knowledge there exists no profound or comprehensive framework nor model to guide system designers, to exploit the full potential of integrating users in the system’s reasoning process by design. In this paper, we present a multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components, has been derived from an iterative mixed-methods development and evaluation procedure, including expert workshops and extensive multidisciplinary literature reviews. Users are accordingly integrated into the whole process from system reasoning until decision making through the following actionable design strategies: (1) Empower: Enabling them to understand the result generation and implications, (2) Encourage: encouraging them to question and reflect system outcomes and to get involved in the generation process and (3) Engage: enabling them to take an active role by facilitating and providing opportunities for user control. The framework offers support to designers of personal health-related DSS and RS in properly integrating users into their systems.

HealthRecSys 2020 was the 5th International Workshop on Health Recommender Systems held in conjunction with the 14th ACM Conference on Recommender Systems. This workshop followed the previous workshop in 2019 [4] and focused on the application and potentials of recommender systems on health promotion, health care, and health-related topics. By engaging in the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure. This year, in particular, COVID-19-related contributions were discussed.

HealthRecSys 2019 was the 4th International Workshop on Health Recommender Systems held in conjunction with the 2019 ACM Conference on Recommender Systems in Copenhagen, Denmark. This workshop followed on from of the previous workshop in 2018 [4] and focused on the application and potentials of recommender systems on health promotion, health care and health-related topics. By engaging the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure.

Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users' feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users' expectations of---and their trust in---RS.

In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.

The 3rd International Workshop on Health Recommender Systems was held in conjunction with the 2018 ACM Conference on Recommender Systems in Vancouver, Canada. Following the two prior workshops in 2016 [4] and 2017 [2], the focus of this workshop is to deepen the discussion on health promotion, health care as well as health related methods. This workshop also aims to strengthen the HealthRecSys community, to engage representatives of other health domains into cross-domain collaborations, and to exchange and share infrastructure.

A growing number of studies in the computer science and engineering communities are addressing mood, an affective phenomenon related but not equivalent to emotion. While emotion has been investigated intensely in the affective computing domain, the characteristics and applications of mood are relatively unexplored. Through a bottom-up approach, this paper aims to identify in which areas and for what purposes computer scientists and other researchers in the ACM and IEEE communities are studying mood. Based on a literature review of 1,264 peer-reviewed publications, this paper proposes a taxonomy of mood research in affective computing. Despite a wide range of applications and domains, core themes of mood research relate to identifying users' mood, influencing it, or helping users to communicate their mood to others. The conceptualization and definition of mood, however, vary between the studies surveyed and sometimes can fall considerably far from the psychological concept of mood in affect research. In several instances, researchers use the terms mood and emotion interchangeably and do not sufficiently discuss the implications both for their measurements and for the design of affective-computing systems as well. With our study, we aim to contribute a clearer conceptualization of mood research and to provide researchers with a broad overview of the research as well as areas of applications in which mood is addressed.

The 2017 Workshop on Health Recommender Systems was held in conjunction with the 2017 ACM Conference on Recommender Systems in Como, Italy. Following the fists workshop in 2016, the focus of this workshop was on enhancing the results of the first workshop by elaborating discussions on the topics, attracting scientist from other domains, finding cross-domain collaboration, and establishing shared infrastructures.