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.

In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences. In this work, we conduct a systematic study on user-cluster targeted data poisoning attacks on Matrix Factorisation (MF)-based RS, where an adversary injects fake users with falsely crafted user-item feedback to promote an item to a specific user cluster. We analyze how user and item feature matrices change after data poisoning attacks and identify the factors that influence the effectiveness of the attack on these feature matrices. We demonstrate that the adversary can easily target specific user clusters with minimal effort and that some items are more susceptible to attacks than others. Our theoretical analysis has been validated by the experimental results obtained from two real-world datasets. Our observations from the study could serve as a motivating point to design a more robust RS.

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.

How to quickly and reliably learn the preferences of new users remains a key challenge in the design of recommender systems. In this paper we introduce a new type of online learning algorithm, cluster-based bandits, to address this challenge. This exploits the fact that users can often be grouped into clusters based on the similarity of their preferences, and this allows accelerated learning of new user preferences since the task becomes one of identifying which cluster a user belongs to and typically there are far fewer clusters than there are items to be rated. Clustering by itself is not enough however. Intra-cluster variability between users can be thought of as adding noise to user ratings. Deterministic methods such as decision-trees perform poorly in the presence of such noise. We identify so-called distinguisher items that are particularly informative for deciding which cluster a new user belongs to despite the rating noise. Using these items the cluster-based bandit algorithm is able to efficiently adapt to user responses and rapidly learn the correct cluster to assign to a new user.