The first part of this thesis focuses on maximizing the overall recommendation accuracy. This accuracy is usually evaluated with some user-oriented metric tailored to the recommendation scenario, but because recommendation is usually treated as a machine learning problem, recommendation models are trained to maximize some other generic criteria that does not necessarily align with the criteria ultimately captured by the user-oriented evaluation metric. Recent research aims at bridging this gap between training and evaluation via direct ranking optimization, but still assumes that the metric used for evaluation should also be the metric used for training. We challenge this assumption, mainly because some metrics are more informative than others. Indeed, we show that models trained via the optimization of a loss inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even when accuracy is measured with metrics other than RBP. However, the superiority of this RBP-inspired loss stems from further benefiting users who are already well-served, rather than helping those who are not. This observation inspires the second part of this thesis, where our focus turns to helping non-mainstream users. These are users who are difficult to recommend to either because there is not enough data to model them, or because they have niche taste and thus few similar users to look at when recommending in a collaborative way. These differences in mainstreamness introduce a bias reflected in an accuracy gap between users or user groups, which we try to narrow.

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