CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach

The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous runtime system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness , and the results show that it is fast, scalable and improves serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

Cinema-going trajectories in the digital age

The activity of cinema-going constantly evolves and gradually integrates the use of digital data and platforms to become more engaging for the audiences. Combining methods from the fields of Human Computer Interaction and Film Studies, we conducted two workshops seeking to understand cinema audiences’ digital practices and explore how the contemporary cinema-going experience is shaped in the digital age. Our findings suggest that going to the movies constitutes a trajectory during which cinemagoers interact with multiple digital platforms. At the same time, depending on their choices, they construct unique digital identities that represent a set of online behaviours and rituals that cinemagoers adopt before, while and after cinema-going. To inform the design of new, engaging cinemagoing experiences, this research establishes a preliminary map of contemporary cinema-going including digital data and platforms. We then discuss how audiences perceive the potential improvement of the experience and how that would lead to the construction of digital identities

Identifying rail asset maintenance processes: a human-centric and sensemaking approach

Efficient asset maintenance is key for delivering services such as transport. Current rail maintenance processes have been mostly reactive with a recent shift towards exploring proactive modes. The introduction of new ubiquitous technologies and advanced data analytics facilitates the embedding of a ‘predict-and-prevent’ approach to managing assets. Successful, user-centred integration of such technology is still, however, a sparsely understood area. This study reports results from a set of interviews, based on Critical Decision Method, with rail asset maintenance and management experts regarding current procedural aspects of asset management and maintenance. We analyse and present the results from a human-centric sensemaking timeline perspective. We found that within a complex sociotechnical environment such as rail transport, asset maintenance processes apply not just at local levels, but also to broader, strategic levels that involve different stakeholders and necessitate different levels of expertise. This is a particularly interesting aspect within maintenance that has not been discussed as of yet within a process-based and timeline-based models of asset maintenance. We argue that it is important to consider asset maintenance activities within both micro (local) and macro (broader) levels to ensure reliability and stability in transport services. We also propose that the traditionally distinct notions of individual, collaborative and artefact-based sensemaking are in fact all in evidence in this sensemaking context, and argue that a more holistic view of sensemaking is therefore appropriate by placing these results within an amended Recogntion Primed Decsion making model

An Empirical Framework for Understanding Human-Technology Interaction Optimisation for Route Planning

A number of interactive systems have been developed in the past to simulate or improve optimised route planning as part of problem solving (e.g. Vehicle Routing Problems (VRPs)) focussing mainly in the utilisation of computational algorithms. Main reasons for developing such interactive systems is that they combine the strengths both computerised systems and humans have, to aid the generation of optimal solutions and promote green logistics. Under a joint-cognitive perspective, the system and the human operator (user) become parts of a single ecosystem, co-operating to complete a task and in which cognitive technologies aid them to reach a decision. This paper reports the performance-based design of such an interactive tool that supports optimisation in route planning. It aims to identify human performance, behaviour and opportunities for designing innovative usercentred interactive optimisation tools for route planning. Twenty-six users evaluated the interactive route planner. Results suggest that switching strategies while planning routes lead to increase in route optimality while providing different levels of control for the user. Results lead to the extension of a joint-cognitive approach framework for optimisation routing problems that takes into account both performance metrics and contextual factors such as changes within the task environment. Related implications to optimisation systems’ design and evaluation are also discussed with a particular focus on how new ubiquitous navigation technologies can be improved to promote cooperation and more optimal route planning