Despite the importance of context in Recommender Systems (RSs) more generally, and its clear applicability in the food domain, most existing research focuses on single contextual factors, and only considers simple extrinsic factors such as location and time. No RSs research has systematically explored the impact of multiple dynamic factors, or investigated the effect of emotion in determining people’s eating, recipe rating and nutritional intake behaviour. To bridge these gaps, we conducted a comprehensive large-scale (n=397) crowdsourced experimental study to uncover the intricate relationship between various simulated contextual factors and users’ subsequent recipe rating and implied nutritional intake behaviour. We further aimed to explore how these contextual factors can be incorporated to improve recommendation performance. Four distinct types of contextual factors were investigated: seasonal, emotional, busyness and physical activity, encompassing a total of seven elements. Our findings show that people’s eating preferences and the likelihood of them choosing to eat healthy recipes vary depending on the simulated context they find themselves in. Moreover, we demonstrate how these contextual features can be used to significantly improve recipe rating prediction performance. Our research has implications for the future development of food RSs, and shows that emotion-aware systems could lead to better healthy food recommendations.

Employers now acknowledge the crucial role of employee wellbeing in promoting productivity, positive relationships, and engagement, as well as its impact on absenteeism and presenteeism. Consequently, there is an increasing need for affordable, evidence-supported, scalable innovative approaches to improve employee wellness. This paper presents usage analysis of a digital employee wellbeing platform, created by Inspire, a mental health social enterprise. The platform has several self-help components, including a chatbot that delivers mental health self-assessments, CBT -based e-learning modules, and a mood tracker. Analysis was conducted using the machine learning technique k-means clustering and descriptive analytics. Through the analysis of user tenure (i.e. the time interval between the first and last day of a user who engaged with the platform), total interactions, daily interactions, and number of unique days on the platform, K-means clustering successfully identified three user groups: short-term (95.5% of users), intermediate (3.4% of users), and long-term users (1.1 % of users). By using these analysis techniques, we can understand how employees utilize a digital employee wellbeing platform, helping to design more effective and personalized solutions.

Emotions, and emotional expression, have a broad influence on social interactions and are thus a key factor to consider in developing social robots. This study examined the impact of life-like affective facial expressions, in the humanoid robot Zeno, on children’s behaviour and attitudes towards the robot. Results indicate that robot expressions have mixed effects depending on participant gender. Male participants interacting with a responsive facially expressive robot showed a positive affective response and indicated greater liking towards the robot, compared to those interacting with the same robot maintaining a neutral expression. Female participants showed no marked difference across the conditions. We discuss the broader implications of these findings in terms of gender differences in human–robot interaction, noting the importance of the gender appearance in robots (in this case, male) and in relation to advancing the understanding of how interactions with expressive robots could lead to task-appropriate symbiotic relationships.

This paper addresses knowledge-based authentication systems in self-service technology, presenting the design and evaluation of the Visual Identification Protocol (VIP). The basic idea behind it is to use pictures instead of numbers as a means for user authentication. Three different authentication systems based on images and visual memory were designed and compared with the traditional Personal Identification Number (PIN) approach in a longitudinal study involving 61 users. The experiment addressed performance criteria and subjective evaluation. The study and associated design exploration revealed important knowledge about users, their attitudes towards and behaviour with novel authentication approaches using images. VIP was found to provide a promising and easy-to-use alternative to the PIN. The visual code is easier to remember, preferred by users and potentially more secure than the numeric code. Results also provided guidelines to help designers make the best use of the natural power of visual memory in security solutions.