SousChef System for Personalized Meal Recommendations: A Validation Study

Nutrition is an essential part of our life. A healthy diet can help to prevent several chronic diseases like diabetes, obesity, cancer, and cardiovascular diseases, being influenced by social, cultural, and economic factors. Meal recommender systems are a trend to assist people in finding new recipes to cook and adopt healthier eating habits. However, food choice is complex and driven by multiple factors which need to be reflected in the personalization process of these systems to ensure their adoption. We present SousChef, a meal recommender system that can help to plan multiple meals considering an individual’s food preferences, restrictions, and nutritional needs. Our approach uses recipes rather than individual food items, limiting recommendations to tasteful and culturally acceptable food combinations. Several experiments were performed to evaluate the system from different perspectives: nutritional, food preferences, and restrictions, and the recommendations’ variability. Our results highlight the importance of using extensive and diverse content in recommendations to meet food preferences, restrictions, and nutritional needs of people with different characteristics.The authors would like to acknowledge the financial support obtained from the project Future Yämmi, co-funded by Compete 2020, Lisboa 2020, Portugal 2020 and the European Union, through the European Regional Development Fund (FEDER). Elsa F. Vieira (Ref CEECIND/03988/2018) thanks FCT (Fundação para a Ciência e Tecnologia) for funding through the Individual Call to Scientific Employment Stimulus, and to REQUIMTE/LAQV.info:eu-repo/semantics/publishedVersio

Design And Lab Experiment Of A Stress Detection Service Based On Mouse Movements

Workplace stress can negatively affect the health condition of employees and with it, the performance of organizations. Although there exist approaches to measure work-related stress, two major limitations are the low resolution of stress data and its obtrusive measurement. The current work applies design science research with the goal to design, implement and evaluate a Stress Detection Service (SDS) that senses the degree of work-related stress solely based on mouse movements of knowledge workers. Using van Gemmert and van Galen’s stress theory and Bakker and Demerouti’s Job Demands-Resource model as justificatory knowledge, we implemented a first SDS prototype that senses mouse movements and perceived stress levels. Experimental results indicate that two feature sets of mouse movements, i.e. average deviation from an optimal mouse trajectory and average mouse speed, can classify high versus low stress with an overall accuracy of 78%. Future work regarding a second build-and-evaluate loop of a SDS, then tailored to the field setting, is discussed

Collecting a dataset of information behaviour in context

Contains fulltext : 127981.pdf (preprint version ) (Open Access)CARR '14 : 4th Workshop on Context-Awareness in Retrieval and Recommendation, April 13, 2014 Amsterda