218 citations · 295 across the 9 of their papers we have counts for
5 papers · 1 filter
Debiased Explainable Pairwise Ranking from Implicit Feedback
Khalil Damak, Sami Khenissi, Olfa Nasraoui
Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper…
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
Sami Khenissi, Mariem Boujelbene, Olfa Nasraoui
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to…
Modeling and Counteracting Exposure Bias in Recommender Systems
Sami Khenissi, Olfa Nasraoui
What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive…
An Explainable Autoencoder For Collaborative Filtering Recommendation
Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborat…
SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
Khalil Damak, Olfa Nasraoui
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models…