  A major challenge in recommender systems is handling new users, whom are also
called $\textit{cold-start}$ users. In this paper, we propose a novel approach
for learning an optimal series of questions with which to interview cold-start
users for movie recommender systems. We propose learning interview questions
using Deep Q Networks to create user profiles to make better recommendations to
cold-start users. While our proposed system is trained using a movie
recommender system, our Deep Q Network model should generalize across various
types of recommender systems.
