Employing Participatory Methods to Engage an Under-Researched Group: opportunities and challenges

In this article, we report on our experience of working on an exploratory project where the primary objective was to involve homeless service users with food-based participatory qualitative approaches. The project FLM aimed to explore food experiences and behaviours in a sample of users of homelessness services in a south west UK coastal city, in order to create solutions to improve their wellbeing. A mixture of qualitative methods was used, including observations, photo-elicitation and focus group discussions. We aimed to be participatory and ‘creative’ in our approach and in our analysis. Here, we focus on detailing and critiquing our approach to the collection and analysis of data.</jats:p

Pattern mining for label ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog

Pattern Mining for Label Ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog