Description
Robot advisory is a hot topic in Banking and Finance nowadays. The quality of any Robot relies on its ability to anticipate the choices of customers and engage them toward an action. For this reason recommendation systems are gaining ground in banking sector as an alternative or supplementary approach to classical Portfolio Selection models. In this talk I show how to build recommendation systems in Python using two different ideas, one inspired by graph theory, and the other by item embedding.