Towards Life-long Personalization Across Multiple Devices: The Case of Personal Career Management

Consider the following scenario. Sammy is a professional software developer, and although content with his current job, is also keen to keep his skills up to date and to keep a look out for new career opportunities in both his current field of work (i.e. as a software developer) as well as potential new fields of work (e.g. as either a developer specialising in user-models and mobile applications; or as a project manager). Sammy has undergone many years of education, having completed 6 years of primary school, another 6 years of high school, 4 years at a University, and a number of speciality post-graduate short-term courses. He has much of these 16+ years of education (as well as details on his past job positions and his possessed skills) condensed into a 2 page curriculum-vitae. Sammy has an inkling that having 16+ years of education and experience, condensed into 2 pages of curriculum-vitae might be missing something, but he can't quite put his finger on what (or just how much). Now consider the following vision: Sammy has a life-long user model containing details of his past education and employment, and at a granularity that contains individual course names and all the topics covered within each course, according to a given curriculum (be that a national school curriculum, a university-specific curriculum, or other), as well as details of past work experiences (types of systems, programming languages etc). He has just registered to an online career web service and installed a career management application onto his favourite mobile device and is keen to look at what career prospects might exist for him both now and in 5 years time. Using data contained within his life-long user model, the system is able to quickly configure an application-specific persona that is used to filter thousands of job listings from a 3 rd -party server. He then commences to browse these personalised listings on his mobile device whenever he has a few spare minutes of time (e.g. during his bus trip to and from work) and notes to himself the importance of being able to efficiently consume information on small screen devices and in busy mobile contexts, as well as the ability to easily pause and resume tasks when the user is mobile. He also appreciates how a number of different and diverse data sets (e.g. his own life-long user model and the 3 rd -party job listings) are being used to create him a personalised list of easily browsed career opportunities within his organisation, the city he lives in, as well as more globally; and the way that educational opportunities also link back to current weaknesses in his career management profile.