  We consider the problem of online collaborative filtering in the online
setting, where items are recommended to the users over time. At each time step,
the user (selected by the environment) consumes an item (selected by the agent)
and provides a rating of the selected item. In this paper, we propose a novel
algorithm for online matrix factorization recommendation that combines linear
bandits and alternating least squares. In this formulation, the bandit feedback
is equal to the difference between the ratings of the best and selected items.
We evaluate the performance of the proposed algorithm over time using both
cumulative regret and average cumulative NDCG. Simulation results over three
synthetic datasets as well as three real-world datasets for online
collaborative filtering indicate the superior performance of the proposed
algorithm over two state-of-the-art online algorithms.
