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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 model. Personalized recommender systems are particularly prone to violati
Externí odkaz:
http://arxiv.org/abs/2308.15230
Autor:
Vassøy, Bjørnar, Langseth, Helge
In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, converse
Externí odkaz:
http://arxiv.org/abs/2305.09330
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 recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several addi
Externí odkaz:
http://arxiv.org/abs/1812.01276
Akademický článek
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