Optimal Sequential Recommendations: Exploiting User and Item Structure

Given the importance of these recommendation algorithms, it makes sense to try to design optimal ones. A basic criterion for optimality, that captures the first-order experience of users in a recommendation system, is to maximize the proportion of recommendations that are liked, 1 similar to [11, 23] The goal of this paper is to gain insight into the design of recommendation algorithms by finding a statistically optimal algorithm within the context of a natural model for recommendation systems. One of our findings is that the best way to obtain information about users and items in order to make good recommendations depends on the time horizon and its relation to various system parameters including the number of users, the diversity of users, and richness of the items; there are a number of operating regimes depending on these parameters. It goes without saying that the nature of any insight obtained is intertwined with the choice of model. We use the same model as [11], closely related to those studied in [10, 12]. The model is different from those in other papers on the topic; we now motivate its key features.