  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.
