1 code implementation • 25 Jul 2023 • Roger Zhe Li, Julián Urbano, Alan Hanjalic
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems.
1 code implementation • 4 Jun 2021 • Roger Zhe Li, Julián Urbano, Alan Hanjalic
Most methods following this approach aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance.
1 code implementation • 2 Feb 2021 • Roger Zhe Li, Julián Urbano, Alan Hanjalic
In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users.
1 code implementation • 27 May 2019 • Julián Urbano, Harlley Lima, Alan Hanjalic
Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random noise because of the selection of topics.
no code implementations • 15 Apr 2019 • Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space.
1 code implementation • 12 Feb 2018 • Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.