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Found 6 papers, 5 papers with code
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
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.
New Insights into Metric Optimization for Ranking-based Recommendation
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.
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
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.
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors
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.
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
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.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
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.
