Author:
McCreadie Richard,Perakis Konstantinos,Srikrishna Maanasa,Droukas Nikolaos,Pitsios Stamatis,Prokopaki Georgia,Perdikouri Eleni,Macdonald Craig,Ounis Iadh
Abstract
AbstractRecent advances in Big Data and Artificial Intelligence have created new opportunities for AI-based agents, referred to as Robo-Advisors, to provide financial advice and recommendations to investors. In this chapter, we will introduce the concept of investment recommendation and describe how automated services for this task can be developed and tested. In particular, this chapter covers the following core topics: (1) the legal landscape for investment recommendation systems, (2) what financial asset recommendation is and what data it needs to function, (3) how to clean and curate that data, (4) approaches to build/train asset recommendation models and (5) how to evaluate such systems prior to putting them into production.
Publisher
Springer International Publishing
Reference45 articles.
1. Aggarwal, C. C., et al. (2016). Recommender systems (Vol. 1). Springer.
2. Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. SIGMOD Record, 22(2), 207–216.
3. Bennett, J., Lanning, S., et al. (2007). The Netflix prize. In Proceedings of KDD cup and workshop (Vol. 2007, p. 35). Citeseer.
4. Cragin, M. H., Heidorn, P. B., Palmer, C. L., & Smith, L. C. (2007). An educational program on data curation.
5. Curry, E., Freitas, A., & O’Riáin, S. (2010). The role of community-driven data curation for enterprises. In Linking enterprise data (pp. 25–47). Springer.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献