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.

翻译：本论文第一部分聚焦于最大化整体推荐准确率。该准确率通常通过针对推荐场景定制的用户导向指标进行评估，但由于推荐通常被视为机器学习问题，推荐模型的训练目标往往是最大化某些与用户导向评估指标未必一致的通用准则。近期研究试图通过直接排序优化来弥合训练与评估之间的差距，但仍假设评估所用的指标也应作为训练指标。我们对此假设提出质疑，主要因为某些指标的信息量大于其他指标。事实上，研究表明，通过优化基于排名偏置精度（RBP）的损失函数训练的模型往往能获得更高的准确率，即使准确率是通过RBP以外的指标衡量的。然而，这种基于RBP的损失函数的优势，源于进一步惠及已获得良好推荐的用户，而非帮助未得到充分服务的用户。这一观察引出了本论文的第二部分，我们将重点转向帮助非主流用户。这些用户难以获得推荐，其原因包括：缺乏足够的数据进行建模，或具有小众偏好导致协同推荐时缺乏相似用户可供参考。主流程度的差异引入了偏差，表现为用户或用户群体之间的准确率差距，我们致力于缩小这一差距。