Developments and cloud-based deployment of software technologies and data broadcast by the user across networks from smart devices as well as location-based social networking has led to significant amounts of data from users being accessible across boundaries in public and private spaces. The availability of this information provides creators with a unique opportunity to produce and provide personalised content for users based on the available information. The breadcrumbs from our searches, page visits, likes, interactions, geo-location, and increasingly private vocal interactions that are mined and moulded to create an algorithmic identity or binary self. The convergence of web based variable font formats and recommender engines for the first time allow us the alter the way people experience type in an agent-oriented fashion that is independent of the third parties with a light footprint contained in a single variable font file. This study explores how we can now humanise the interaction and put the person at the centre of the interaction to influence and evolve the typographic experience based on this available data.
Lecturer, Atlantic Technological University, Ireland
Typography, Type, Variable, Recommender, Adaptive