L’Université Virtuelle de Tunis organise ce mercredi un seminaire animé par :
Olfa Nasraoui, Professor, Endowed Chair of e-commerce
Director, Knowledge Discovery & Web Mining Lab
Dept. of Computer Engineering & Computer Science
Speed School of Engineering
University of Louisville, USA.
Lieu : Amphithéatre à l’INSAT.
Date : mercredi 10 octobre 2018.
Heure : à partir de 14h30.
Inscription ouverte et gratuite.
Intitulé du séminaire :
« Tell me Why? New Research in Building Recommender Systems that Can Explain their Predictions »
Abstract of the excellent talk :
At its core, Big Data is enabled by advanced Machine Learning (ML) models that are now being used
increasingly to enable decision making in many sectors, ranging from e-commerce to health, education, justice, and criminal investigation. Hence, these algorithmic models are starting to directly interact with and affect the daily decisions of more and more human beings. In particular many models are black box models that make predictions without any justification to the user. Without any mechanism to allow humans to understand and question the reasons behind them, Black Box predictions lack justifiability and transparency. In addition, they cannot be scrutinized for possible mistakes and biases.
Therefore, designing explainable machine learning models that facilitate conveying the reasoning behind their predictions, is of great importance. Yet, one main challenge in designing Big Data models is mitigating the trade-off between an explainable technique with moderate prediction accuracy and a more accurate technique with no explainable predictions.
This talk will focus on a special family of Machine Learning models, namely recommender systems; and will present our recent research in building explainability into a selection of state of the art Black Box recommender systems based on Matrix Factorization and Deep Learning.