User profiling from heterogeneous big data by machine learning for Fair digital innovation

Starting date
September 1, 2019
Duration (months)
Computer Science, Department of Engineering for Innovation Medicine
Managers or local contacts
Menegaz Gloria

This research project aims at the digital innovation of fairs following the “Digitising European Industry” initiative promoted by H2020. Specifically, this project aims at the formal definition and implementation of multimedia profiles of users for informing decision-making processes and devising innovative business and marketing models. Big and heterogeneous data collected through both the physical infrastructure and Web access will be analized using ad-hoc machine learning methods and models will be created in synergy with marketing experts and finally validated on the field following suitable performance metrics. The keywork is multimedia profiling, that is at the core of the innovative aspects of the project. All the actors involved in the exhibition, that is both expositors and visitors, will be traced in both the physical and Web word in accordance with the privacy and data protection regulations (UE 2016/679) and information about their behavior will be automatically extracted. The continuous interaction with the marketing experts will ensure the synergy between the ICT and business sections for a better monitoring and optimization of the process accounting for the feedback provided by both potentially providing Veronafiere the leadership in Europe and beyond.
This issue is tackled in a multidisciplinary multi-modal manner: taking advantage of technological advances for user profiling using deep learning methods suitable for dealing with big, fragmented, heterogeneous data (that is exploiting both in-site collected information through the sensors’ backbone that is available in the exposition space and that collected from actor’s Web navigation during the event) for inferring behavioral models of the visitors to be exploited for devising and proposing new marketing strategies and performance measures.


Funds: assigned and managed by the department

Project participants

Gloria Menegaz
Full Professor
Research areas involved in the project
Sistemi intelligenti
Artificial intelligence


Research facilities