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：在个性化推荐系统的设计中，准确建模用户多样化且动态的兴趣仍是一个重大挑战。现有用户建模方法（如单点表示和多元表示）在准确性、多样性、计算成本和适应性方面存在局限性。为克服这些不足，我们引入基于密度的用户表示（Density-based User Representations, DURs），这是一种利用高斯过程回归实现高效多兴趣推荐与检索的新模型。我们的方法GPR4DUR利用DURs捕捉用户兴趣变化性，无需手动调整参数，具备不确定性感知能力，并能在用户规模庞大时保持良好的可扩展性。基于真实离线数据集的实验验证了GPR4DUR的适应性和效率，而采用模拟用户的在线实验则表明，该方法通过有效利用模型不确定性，能够解决探索与利用之间的权衡问题。