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- W3134711533 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." @default.
- W3134711533 created "2021-03-15" @default.
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- W3134711533 date "2020-09-22" @default.
- W3134711533 modified "2023-09-27" @default.
- W3134711533 title "History-Augmented Collaborative Filtering for Financial Recommendations" @default.
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- W3134711533 doi "https://doi.org/10.1145/3383313.3412206" @default.
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