O
Olfa Nasraoui
Researcher at University of Louisville
Publications - 16
Citations - 199
Olfa Nasraoui is an academic researcher from University of Louisville. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 5, co-authored 16 publications receiving 92 citations.
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Proceedings ArticleDOI
Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering
TL;DR: This paper views the RS environment as a chain of events that are the result of interactions between users and the RS, and proposes several debiasing algorithms during thischain of events, and evaluates how these algorithms impact the predictive behavior of theRS, as well as trends in the popularity distribution of items over time.
Journal ArticleDOI
Evolution and impact of bias in human and machine learning algorithm interaction
TL;DR: It is argued that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms’ performance, and three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms.
DissertationDOI
Modeling and Counteracting Exposure Bias in Recommender Systems.
Sami Khenissi,Olfa Nasraoui +1 more
TL;DR: The research findings show the importance of understanding the nature of and dealing with bias in machine learning models such as recommender systems that interact directly with humans, and are thus causing an increasing influence on human discovery and decision making.
Proceedings ArticleDOI
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
TL;DR: A theoretical framework is presented to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots.
Proceedings ArticleDOI
Debiased Explainable Pairwise Ranking from Implicit Feedback
TL;DR: In this paper, an explainable loss function and a corresponding matrix factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) are proposed to generate recommendations along with item-based explanations.