20 citations · 29 across the 4 of their papers we have counts for
4 papers
Exploring How Fair Model Representations Relate to Fair Recommendations
Bjørnar Vassøy, Benjamin Kille, Helge Langseth
One of the many fairness definitions pursued in recent recommender system research targets mitigating demographic information encoded in model representations. Models optimized for…
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
Bjørnar Vassøy, Helge Langseth, Benjamin Kille
An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user's gender or age should not influence the mod…
Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
Bjørnar Vassøy, Helge Langseth
In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both f…
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
Bjørnar Vassøy, Massimiliano Ruocco, Eliezer de Souza da Silva +1
In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendati…