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. Existing methods to mitigate mainstream bias do not explicitly model the importance of these non-mainstream users or, when...

Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. 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. We propose NAECF,...

New Insights into Metric Optimization for Ranking-based Recommendation

Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies...