Modelling User Behaviors with Evolving Users and Catalogs of Evolving Items.

Leonardo Cella

Abstract: Recommender systems are used to suggest users products that they would not be able to find by themselves. State of the art algorithms assume that items have static features, however this assumption does not always correspond to reality. There are challenging and still unexplored domains, where not only users but also items have properties that evolve continuously over time. In this research we aim to overcome these limitations by suggesting to model evolution of users and items as a reinforcement learning problem. As use case we will refer to the recommendation problem applied to the financial domain, where items' (contracts) features evolve continuously according to "market laws".