Business insights from interactive recommendation systems
: a proof of concept

  • Jean Albrecq

Student thesis: Master typesMaster in Computer Science Professional focus in Data Science


Recommender systems are on most e-commerce websites, helping customers to find the best product or service. In this thesis, we focus on interactive recommenders (IR) which are used to help customers with complex products by asking them simple questions about their needs. Those recommenders can be combined with event driven architecture (EDA) that saves each state of the system lifetime. This combination allows to generate much more useful data and to be able to analyze it more effectively. Recommender combined with EDA are very powerful and generate lots of data about the user’s needs but still very little is known about how to best take advantage of these data. After defining which data is relevant to the business, we have built a proof of concept to show how and what data can be extracted from these systems. This proof-of-concept extract data from an EDA-based IR to show a new way to benefit fully from IRS-generated data.
Date of Award31 Aug 2021
Original languageEnglish
Awarding Institution
  • University of Namur
SupervisorPatrick Heymans (Supervisor)


  • Recommander systems
  • Event driven architecture
  • business intelligence
  • business insight
  • event
  • proof of concept

Cite this