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：准确建模用户多样且动态的兴趣仍是设计个性化推荐系统面临的重要挑战。现有的用户建模方法（如单点表示与多点表示）在准确性、多样性、计算成本及适应性方面存在局限性。为克服这些缺陷，我们提出基于密度的用户表示（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。我们的方法GPR4DUR通过DURs捕捉用户兴趣变化，无需手动调参，具备不确定性感知能力，并可在大规模用户场景下高效扩展。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，该方法通过有效利用模型不确定性，成功实现了探索与利用的平衡。