Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation

ArXiv ID: 2308.01208 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: collaborative filtering, personalized decay functions, non-stationarity, time-dependent algorithms, Financial Products / Banking

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper employs moderate mathematical complexity through graph neural network formulations and embedding decomposition but focuses heavily on empirical validation using a proprietary real-world dataset from BNP Paribas with clear benchmark comparisons and ablation studies.

  flowchart TD
    A["Research Goal:<br>Dynamic Financial Product Recommendations"] --> B{"Data Input:<br>BNP Paribas Proprietary Dataset"}
    B --> C["Preprocessing &<br>Time-Stamping Interactions"]
    C --> D["Adaptive Collaborative Filtering:<br>Personalized Time Decay Functions"]
    D --> E["Model Training:<br>Handling Non-Stationary Data"]
    E --> F["Evaluation:<br>Comparison with State-of-the-Art Benchmarks"]
    F --> G["Outcome:<br>Significant Improvement in Recommendation Accuracy"]