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History-Augmented Collaborative Filtering for Financial Recommendations

Author

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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)

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.

Suggested Citation

  • Baptiste Barreau & Laurent Carlier, 2020. "History-Augmented Collaborative Filtering for Financial Recommendations," Post-Print hal-03144669, HAL.
    • Handle: RePEc:hal:journl:hal-03144669
    DOI: 10.1145/3383313.3412206
    Note: View the original document on HAL open archive server: https://hal.science/hal-03144669
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    References listed on IDEAS

    as
    1. Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015. "Deep learning," Nature, Nature, vol. 521(7553), pages 436-444, May.
    2. Wright, Dominic & Capriotti, Luca & Lee, Jacky, 2018. "Machine learning and corporate bond trading," Algorithmic Finance, IOS Press, vol. 7(3-4), pages 105-110.
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    Cited by:

    1. Chen, Song & Qiu, Yongqin & Li, Jingmao & Fang, Kan & Fang, Kuangnan, 2023. "Precision marketing for financial industry using a PU-learning recommendation method," Journal of Business Research, Elsevier, vol. 160(C).

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    More about this item

    Keywords

    matrix factorization; collaborative filtering; context-aware; time; neural networks;
    All these keywords.

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