Projects

Multi-Task Learning for Customer Understanding

Partner: Swisscom
Partner contact: Dan-Cristian Tomozei
EPFL laboratory: Signal Processing Laboratory (LTS4)
EPFL contact: Prof. Pascal Frossard, Nikolaos Dimitriadis

Customer understanding is a ubiquitous and multifaceted business application whose mission lies in providing better experiences to customers by recognising their needs. A multitude of tasks, ranging from churn prediction to accepting upselling recommendations, fall under this umbrella. Common approaches model each task separately and neglect the common structure some tasks may share. The purpose of this project is to leverage multi-task learning to better understand the behaviour of customers by modeling similar tasks into a single model. This multi-objective approach utilises the information of all involved tasks to generate a common embedding that can be beneficial to all and provide insights into the connection between different user behaviours, i.e. tasks. The project will provide data-driven insights into customer needs leading to retention as well as revenue maximisation while providing a better user experience.

Topics • Machine Learning • Digital Information