PhD student in Personalisation in Cyber Physical Social Systems: the case of personalised recomme...

July 24 2017
Position Type
Full Time
Organization Type

The ADAPT research group in the “Human Dynamics in Cognitive Environments” (HDCE, formally DKD) unit of LIST, is looking for a future PhD. Student, in the domain of Personalisation, Recommender Systems and Cyber Physical Social Systems.

The HDCE unit from the IT R&D department is working on smart interactive collaborative systems to support design, decision, and problem solving in systemic and complex situations involving cooperation between artificial and human agents as well as massive data loads. More particularly, the unit focuses on data sensing, knowledge elicitation, model-based systems, feedback from context, user profiling and human-to-human interaction analysis, Natural User Interfaces and embedded assessment. The successful candidate will join, as a PhD. Student, the ADAPT research group, which focuses on Knowledge-based Context-Aware Adaptive Systems.


In Cyber Physical Social Systems (CPSS), users of personalized services evolve in an environment inducing constraints making the personalisation problem more complex. Indeed the user evolves in a physical space, with other persons. His behaviour is constrained by the first, while influencing and being influenced by the later. Additionally, the physical space has itself a given purpose, calling for expected specific behaviours of people inside. This particular context leads to formulate the personalisation problem differently, as a function of the user, the physical space, the crowd of other persons in the physical space, the IT application implementing personalised services and the global context.

The PhD. objective is to provide a theoretical contribution to personalisation in CPSS, and applying it to the case of personalized recommendations and guidance in physical spaces. The latter will in particular be formulated as a multi-objective optimisation problem. Space syntax and museum's visiting styles theories will be explored to model the movements and behaviour of the crowd in the physical space of the CPSS. New personalised guidance algorithms based on paths / point of interests recommendations will be designed following the developed theory, and evaluated both in simulation and on real-world use-cases. Solutions exploiting semantic web technologies will be privileged.

This job comes from a partnership with Science Magazine and Euraxess