Recommender systems are increasingly used in high-risk application domains, including healthcare. It has been shown that explanations are crucial in this context to support decision-making. This paper explores how to explain call recommendations to nursing home staff, providing insights into call priority, notifications, and resident information. We present the design and implementation of a recommender engine and a mobile application designed to support call recommendations and explain these recommendations that may contribute to residents’ safety and quality of care. More specifically, we report on the results of a user-centered design approach with residents (N=12) and healthcare professionals (N=4), and a final evaluation (N=12) after four months of deployment. The results show that our design approach provides a valuable tool for more accurate and efficient decision-making. The overall system encourages nursing home staff to provide feedback and annotate, resulting in more confidence in the system. We discuss usability issues, challenges, and reflections to be considered in future health recommender systems.

introduction Share on Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022) Authors: Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , David Graus Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Mesut Kaya Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Francisco Gutiérrez Computer Science, KU Leuven, Belgium Computer Science, KU Leuven, BelgiumView Profile , Sepideh Mesbah Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Chris Johnson Indeed, United States Indeed, United StatesView Profile Authors Info & Claims RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsSeptember 2022Pages 671–674https://doi.org/10.1145/3523227.3547414Published:13 September 2022Publication History 0citation112DownloadsMetricsTotal Citations0Total Downloads112Last 12 Months42Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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Recommender algorithms play an active role in many everyday activities. However, personalized recommendations often produce negative experiences due to a lack of awareness, control, or transparency. Allowing users to materialize their algorithmic imaginaries exposes how they experience, perceive, and imagine recommender algorithms. Moreover, it can unearth novel and previously unattended design opportunities for tangible interactions with algorithms. Therefore, we explored how 15 users of a famous movie recommender system materialized tangible designs to reflect and discuss their algorithmic imaginaries during co-design workshops and interviews. Using thematic analysis, we identified two forms of algorithmic imaginaries that can inspire tangible interactions with recommender algorithms: metaphoric and datafied representations. Complementary themes exposed the influence of contextual factors and diverse negative attitudes towards personalized movie recommendations. Based on these findings, we suggest design opportunities and suggestions for improving the algorithmic experience of movie recommendations and similar systems through tangible user interfaces.

Job mediation services can assist job seekers in finding suitable employment through a personalised approach. Consultation or mediation sessions, supported by personal profile data of the job seeker, help job mediators understand personal situation and requests. Prediction and recommendation systems can directly provide job seekers with possible job vacancies. However, incorrect or unrealistic suggestions, and bad interpretations can result in bad decisions or demotivation of the job seeker. This paper explores how an interactive dashboard visualising prediction and recommendation output can help support the dialogue between job mediator and job seeker, by increasing the "explainability" and providing mediators with control over the information that is shown to job seekers.

The dynamics of the labor market and the tasks with which jobs are being composed are continuously evolving. Job mobility is not evident, and providing effective recommendations in this context has also been found to be particularly challenging. In this paper, we present Labor Market Explorer, an interactive dashboard that enables job seekers to explore the labor market in a personalized way based on their skills and competences. Through a user-centered design process involving job seekers and job mediators, we developed this dashboard to enable job seekers to explore job recommendations and their required competencies, as well as how these competencies map to their profile. Evaluation results indicate the dashboard empowers job seekers to explore, understand, and find relevant vacancies, mostly independent of their background and age.

This paper presents an evaluation of a learning dashboard that supports the dialogue between a student and a study advisor. The dashboard was designed, developed, and evaluated in collaboration with study advisers. To ensure scalability to other contexts, the dashboard uses data that is commonly available at any higher education institute. It visualizes the grades of the student, an overview of the progress through the year, his/her position in comparison with peers, sliders to plan the next years and a prediction of the length of the bachelor program for this student in years based on historic data. The dashboard was deployed at KU Leuven, Belgium and used in September 2017 to support 224 sessions between students and study advisers. We observed twenty of these conversations. We also collected feedback from 101 students with questionnaires. Results of our observations indicate that the dashboard primarily triggers insights at the beginning of a conversation. The number of insights and the level of these insights (factual, interpretative and reflective) depends on the context of the conversation. Most insights were triggered in conversations with students doubting to continue the program, indicating that our dashboard is useful to support difficult decision-making processes.

This paper introduces a service-oriented model for the description of embedded Peer-to-Peer (EP2P) systems and formalizes the proposed model in Maude. The model is organized around the notions of groups of peers and services offered by these groups. We first summarize the main concepts of the model and then present αSMoL, an abstract language with a formal semantics that provides a solid ground to develop tools for the automated analysis and verification of EP2P specifications. We then describe a formalization of αSMoL in Maude, and introduce an example to illustrate both the expressive power of the model and the possibilities of Maude to support automated verification of properties of αSMoL programs.