Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings. This assumption is particularly problematic in finance, where financial products exhibit continuous changes in valuations, leading to frequent shifts in client interests. These evolving interests, summarized in the past client-product interactions, see their utility fade over time with a degree that might differ from one client to another. To address this challenge, we propose a time-dependent collaborative filtering algorithm that can adaptively discount distant client-product interactions using personalized decay functions. Our approach is designed to handle the non-stationarity of financial data and produce reliable recommendations by modeling the dynamic collaborative signals between clients and products. We evaluate our method using a proprietary dataset from BNP Paribas and demonstrate significant improvements over state-of-the-art benchmarks from relevant literature. Our findings emphasize the importance of incorporating time explicitly in the model to enhance the accuracy of financial product recommendation.

翻译：经典推荐系统通常假设历史数据是平稳的，未能考虑用户偏好的动态特性，这一局限性制约了其在时间敏感场景中提供可靠推荐的能力。在金融领域，该假设问题尤为突出——金融产品的估值持续变化，导致客户兴趣频繁转移。这些体现于历史客户-产品交互记录中的动态兴趣，其效用会随时间衰减，且不同客户的衰减程度可能各异。针对这一挑战，我们提出一种基于时间的协同过滤算法，该算法通过个性化衰减函数自适应地降低远距离客户-产品交互的权重。本方法通过建模客户与产品之间的动态协同信号，旨在处理金融数据的非平稳性并生成可靠推荐。我们采用法国巴黎银行专有数据集进行评估，结果表明该方法相较于相关文献中的现有最优基准具有显著改进。研究结果强调了在模型中显式引入时间要素对提升金融产品推荐准确性的关键作用。