  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.
