Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search

Abstract

We revisit the cold-start task for new users of a recommender system whereby a new user is asked to rate a few items with the aim of discovering the user’s preferences. This is a combinatorial stochastic learning task, and so difficult in general. In this paper we propose using Monte Carlo Tree Search (MCTS) to dynamically select the sequence of items presented to a new user. We find that this new MCTS-based cold-start approach is able to consistently quickly identify the preferences of a user with significantly higher accuracy than with either a decision-tree or a state of the art bandit-based approach without incurring higher regret i.e the learning performance is fundamentally superior to that of the state of the art. This boost in recommender accuracy is achieved in a computationally lightweight fashion.