History-Augmented Collaborative Filtering for Financial Recommendations

Baptiste Barreau () and Laurent Carlier ()
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Baptiste Barreau: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay, BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab
Laurent Carlier: BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab

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Abstract: In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes. In this study, we propose a novel collaborative filtering algorithm that captures the temporal context of a user-item interaction through the users' and items' recent interaction histories to provide dynamic recommendations. The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors. The performance and properties of the algorithm are monitored in a series of experiments on a G10 bond request for quotation proprietary database from BNP Paribas Corporate and Institutional Banking.

Keywords: matrix factorization; collaborative filtering; context-aware; time; neural networks (search for similar items in EconPapers)
Date: 2020-09
New Economics Papers: this item is included in nep-cmp
Note: View the original document on HAL open archive server: https://hal.science/hal-03144669
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Published in RecSys '20: Fourteenth ACM Conference on Recommender Systems, Sep 2020, Virtual Event, Brazil. pp.492-497, ⟨10.1145/3383313.3412206⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03144669

DOI: 10.1145/3383313.3412206

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