History-Augmented Collaborative Filtering for Financial Recommendations
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.
Cite
@article{arxiv.2102.13503,
title = {History-Augmented Collaborative Filtering for Financial Recommendations},
author = {Baptiste Barreau and Laurent Carlier},
journal= {arXiv preprint arXiv:2102.13503},
year = {2021}
}