Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.

翻译：在个性化推荐系统设计中，准确建模用户多样且动态的兴趣仍然是一项重大挑战。现有的用户建模方法，如单点表征和多点表征，在准确性、多样性和适应性方面存在局限。为克服这些不足，我们引入密度用户表征（DURs）这一新方法，其利用高斯过程回归（GPR）实现有效的多兴趣推荐与检索。我们的方法GPR4DUR运用DURs来捕捉用户兴趣的变异性而无需人工调参，具备不确定性感知能力，并能良好地扩展到海量用户规模。基于真实世界离线数据集的实验验证了GPR4DUR的适应性与高效性，而使用模拟用户进行的在线实验则证明其能通过有效利用模型不确定性来平衡探索与利用的权衡。