Learning User Preferences in Non-Stationary Environments
This addresses the challenge of adapting recommendation systems to dynamic user preferences, which is crucial for practical applications like e-commerce and streaming services, though it builds incrementally on existing non-stationary bandit literature.
The paper tackles the problem of recommendation systems in non-stationary environments where user preferences change over time, proposing a user-based collaborative filtering algorithm that outperforms static methods in real-world datasets, even in stationary settings.
Recommendation systems often use online collaborative filtering (CF) algorithms to identify items a given user likes over time, based on ratings that this user and a large number of other users have provided in the past. This problem has been studied extensively when users' preferences do not change over time (static case); an assumption that is often violated in practical settings. In this paper, we introduce a novel model for online non-stationary recommendation systems which allows for temporal uncertainties in the users' preferences. For this model, we propose a user-based CF algorithm, and provide a theoretical analysis of its achievable reward. Compared to related non-stationary multi-armed bandit literature, the main fundamental difficulty in our model lies in the fact that variations in the preferences of a certain user may affect the recommendations for other users severely. We also test our algorithm over real-world datasets, showing its effectiveness in real-world applications. One of the main surprising observations in our experiments is the fact our algorithm outperforms other static algorithms even when preferences do not change over time. This hints toward the general conclusion that in practice, dynamic algorithms, such as the one we propose, might be beneficial even in stationary environments.