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, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression 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：精准建模用户多样化和动态变化的兴趣，仍是个性化推荐系统设计中的重大挑战。现有用户建模方法（如单点表征与多点表征）在准确性、多样性、计算成本及适应性方面存在局限。为克服上述缺陷，我们提出基于密度的用户表征（DURs）——一种利用高斯过程回归实现高效多兴趣推荐与检索的新型模型。本方法GPR4DUR通过DURs捕捉用户兴趣变化性，无需人工调参，具备不确定性感知能力，并能在大规模用户场景下良好扩展。基于真实离线数据集的实验验证了GPR4DUR的适应性与效率，而模拟用户在线实验则表明，该方法能够通过有效利用模型不确定性来应对探索-利用权衡问题。