Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, 'good enough' decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks

Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Learning histories, participatory methods and creative engagement for climate resilience

The potential of place-based, historically-informed approaches to drive climate action has not yet been adequately interrogated. Recent scholarly work has focussed on climate communication and the role of arts and humanities-led storytelling in engaging people in climate narratives. Far less has been said about mobilising arts and creativity to build anticipatory climate action. perNor have archival material and pre-twentieth century histories of living with water and flood been widely utilised in this endeavour. This paper reflects on our experiences delivering the UKRI-funded Risky Cities programme and specifically, of developing and utilising a learning histories approach that folds together past, present and future in productive ways so as to learn from the past and the present and rethink the future. Risky Cities uses this approach to develop engagement tools at different scales, evaluating their impact throughout using participant interviews, reflective focus groups, and surveys. Analysing this data, we consistently find that using learning histories as the foundation of arts-led and creative community engagement makes big narratives about global climate change locally meaningful. Crucially, this drives cognitive shifts, behavioural change and anticipatory action for both participants and audiences. Thus, our learning histories approach is an important participatory tool for building climate action, empowerment and resilience