Cifre contract with NSP SmartProfile “Automated and responsible recommendation systems for digital marketing” (August 2022 – July 2025)
- Participants: Konstantin Avrachenkov, Ibtihal El-Mimouni
- Collaborators: Hervé Baile, Julien Musso
SmartProfile is a marketing platform that allows to collect, to enhance and to analyse marketing
data. Digital marketing campaigns continue to expand across all digital channels and media. The
’mass marketing’ strategies implemented by most companies show limits in terms of performance and
acceptance by clients, as well as in terms of their impact on the environment. In opposite to these
practices, we believe that current technologies, particularly in terms of Artificial Intelligence (AI), should
make marketing interactions more efficient and virtuous. Through this research project, we want to create
an alternative solution to mass marketing by switching to an intelligent, automated and eco-responsible
system, which will support the heterogeneity of data and the diversity of sectors, and whose purpose
is to recommend the best content by determining the most relevant target and taking into account the
communication constraints. This contract complements the Cifre thesis of Ibtihal El Mimouni.
NSP SmartProfile contract on the topic “Recommendation systems with heterogeneous data” (1 March 2021 – 31 August 2021)
- Participants: Konstantin Avrachenkov, Ibtihal El Mimouni
- Collaborators: Julien Musso, Hervé Baile
SmartProfile is a marketing platform that allows to collect, to enhance and to analyse marketing data. Typically the data is very heterogeneous (binary data, integer numbers, real numbers, functional data, images, etc). This contract was funding the internship of Ibtihal El Mimouni on the exploratory investigation of recommendation systems with heterogeneous data. This contract has been followed by a Cifre application.
NSP contract “Semi-supervised segmentation” (November 2017 – April 2018)
Participants: Konstantin Avrachenkov, Gagan Deep Singh Chhabra
The project “Semi-supervised segmentation” aimed at applying NEO’s knowledge in semi-supervised segmentation to one of NSP’s databases.
