PURE: An Uncertainty-aware Recommendation Framework for Maximizing Expected Posterior Utility of Platform

Commercial recommendation can be regarded as an interactive process between the recommendation platform and its target users. One crucial problem for the platform is how to make full use of its advantages so as to maximize its utility, i.e., the commercial benefits from recommendation. In this paper, we propose a novel recommendation framework which effectively utilizes the information of user uncertainty over different item dimensions and explicitly takes into consideration the impact of display policy on user in order to achieve maximal expected posterior utility for the platform. We formulate the problem of deriving optimal policy to achieve maximal expected posterior utility as a constrained non-convex optimization problem and further propose an ADMM-based solution to derive an approximately optimal policy. Extensive experiments are conducted over data collected from a real-world recommendation platform and demonstrate the effectiveness of the proposed framework. Besides, we also adopt the proposed framework to conduct experiments with an intent to reveal how the platform achieves its commercial benefits. The results suggest that the platform should cater to the user's preference for item dimensions that the user prefers, while for item dimensions where the user is with high uncertainty, the platform can achieve more commercial benefits by recommending items with high utilities.