Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced nature of users' interests, which span a wide range of diversity, pose a significant challenge in delivering fair recommendations. In practice, user preferences vary significantly; some users show a clear preference toward certain item categories, while others have a broad interest in diverse ones. Even though it is expected that all users should receive high-quality recommendations, the effectiveness of RSs in catering to this disparate interest diversity remains under-explored. In this work, we investigate whether users with varied levels of interest diversity are treated fairly. Our empirical experiments reveal an inherent disparity: users with broader interests often receive lower-quality recommendations. To mitigate this, we propose a multi-interest framework that uses multiple (virtual) interest embeddings rather than single ones to represent users. Specifically, the framework consists of stacked multi-interest representation layers, which include an interest embedding generator that derives virtual interests from shared parameters, and a center embedding aggregator that facilitates multi-hop aggregation. Experiments demonstrate the effectiveness of the framework in achieving better trade-off between fairness and utility across various datasets and backbones.

翻译：推荐系统（RSs）因其卓越的用户兴趣捕捉能力，已在各领域得到广泛应用。然而，用户兴趣的复杂性与细微差异性（涵盖广泛的多样性）为提供公平推荐带来重大挑战。实践中，用户偏好存在显著差异：部分用户对特定物品类别有明确偏好，而另一些用户则对多样化内容抱有广泛兴趣。尽管所有用户都应获得高质量推荐，但推荐系统在应对这种兴趣多样性差异方面的有效性尚未得到充分探索。本研究考察了不同兴趣多样性水平的用户是否受到公平对待。实证实验揭示了固有偏差：兴趣越广泛的用户往往获得更低的推荐质量。为缓解这一问题，我们提出多兴趣框架，采用多个（虚拟）兴趣嵌入而非单一嵌入来表征用户。具体而言，该框架由堆叠的多兴趣表征层构成，包括从共享参数推导虚拟兴趣的兴趣嵌入生成器，以及促进多跳聚合的中心嵌入聚合器。实验表明，该框架能在多个数据集和骨干网络上实现公平性与效用性间的更优权衡。