Dimitris Paraschakis

Ensemble Recommendations via Thompson Sampling: an Experimental Study within e-Commerce

This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. We show how to…