218 citations · 295 across the 9 of their papers we have counts for
9 papers
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
Khalil Damak, Sami Khenissi, Olfa Nasraoui
Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Mask…
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…
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
Olfa Nasraoui, Patrick Shafto
Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation have recently been opened to a…
Explainable Restricted Boltzmann Machines for Collaborative Filtering
Behnoush Abdollahi, Olfa Nasraoui
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the syst…
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
Ehsan Hosseini-Asl, Jacek M. Zurada, Olfa Nasraoui
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. T…