Abstract
Financial asset recommendation (FAR) is an emerging sub-domain of the wider recommendation field that is concerned with recommending suitable financial assets to customers, with the expectation that those customers will invest capital into a subset of those assets. FAR is a particularly interesting sub-domain to explore, as unlike traditional movie or product recommendation, FAR solutions need to analyse and learn from a combination of time-series pricing data, company fundamentals, social signals and world events, relating the patterns observed to multi-faceted customer representations comprising profiling information, expectations and past investments. In this demo we will present a modular FAR platform; referred to as FAR-AI, with the goal of raising awareness and building a community around this emerging domain, as well as illustrate the challenges, design considerations and new research directions that FAR offers. The demo will comprise two components: 1) we will present the architecture of FAR-AI to attendees, to enable them to understand the how’s and the why’s of developing a FAR system; and 2) a live demonstration of FAR-AI as a customer-facing product, highlighting the differences in functionality between FAR solutions and traditional recommendation scenarios. The demo is supplemented by online-tutorial materials, to enable attendees new to this space to get practical experience with training FAR models. VIDEO URL.
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Notes
- 1.
Recommendation services are often referred to as robo-advisors in this context.
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Acknowledgements
This work was in part carried out within the Infinitech project which is supported by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no. 856632. Subsequent platform development was also financially supported via Engineering and Physical Sciences Research Council (EPSRC) Impact Accelerator, part of UK Research and Innovation (UKRI) with grant ref. number EP/X525716/1.
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Sanz-Cruzado, J., Richards, E., McCreadie, R. (2024). FAR-AI: A Modular Platform for Investment Recommendation in the Financial Domain. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_30
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