A Bandit Method Using Probabilistic Matrix Factorization in Recommendation

In recommendation system,sparse data and cold-start user have always been a challenging problem.Using a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and user-item context,we model the recommendation problem as a multi-arm bandit(MAB)problem in this paper.Enabling the engine to recommend while it learns,we adopt probabilistic matrix factorization(PMF) in this strategy learning phase after observing the payoff.In particular,we propose a new approach to get the upper bound statistics out of latent feature matrix.In the experiment,we use two public datasets(Netfilx and MovieLens) to evaluate our proposed model.The model shows good results especially on cold-start users.