User Cold-Start Learning In Recommender Systems Using Monte Carlo Tree Search

We consider 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 quickly discovering the user’s preferences. This is a combinatorial stochastic learning task, and so it is difficult in general. In this paper we study the use of Monte Carlo Tree Search (MCTS) to dynamically select the sequence of items presented to a new user. We find that the 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. The MCTS approach is flexible in the sense that it can be readily extended to incorporate different types of user feedback including explicit ratings, ranked comparisons and missing not at random data.