In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories). As such, real-world recommender systems often explicitly incorporate diversity separately from accuracy. This approach, however, leaves a basic question unanswered: Why is there a trade-off in the first place? We show how the trade-off can be explained via a user's consumption constraints -- users typically only consume a few of the items they are recommended. In a stylized model we introduce, objectives that account for this constraint induce diverse recommendations, while objectives that do not account for this constraint induce homogeneous recommendations. This suggests that accuracy and diversity appear misaligned because standard accuracy metrics do not consider consumption constraints. Our model yields precise and interpretable characterizations of diversity in different settings, giving practical insights into the design of diverse recommendations.

翻译：在推荐场景中，准确度（推荐用户最可能需要的物品）与多样性（推荐覆盖多个类别的物品）的目标之间存在明显的权衡关系。因此，现实中的推荐系统常将多样性作为独立于准确性的指标显式纳入考量。然而，这种方法留下了一个基本问题未解：为何这种权衡关系会存在？我们展示了如何通过用户的消费约束来解释这种权衡——用户通常只会消费被推荐物品中的少数几个。在我们引入的简化模型中，考虑该约束的目标函数会产生多样化推荐，而未考虑该约束的目标函数则会导致同质化推荐。这表明准确性与多样性看似矛盾，是因为标准准确性指标未考虑消费约束。我们的模型对不同场景下的多样性给出了精确且可解释的刻画，为设计多样化推荐提供了实用洞见。