PURE: AN UNCERTAINTY-AWARE RECOMMENDATION FRAMEWORK FOR MAXIMIZING EXPECTED POSTE-RIOR UTILITY OF PLATFORM

Abstract

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 1 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.

1. INTRODUCTION

Commercial recommendation systems have been widely applied among prevalent content distribution platforms such as YouTube, TikTok, Amazon and Taobao. During the interactive process on the recommendation platform, the users may find contents of their interests and avoid the information overload problem with the help of recommendation services. Meanwhile, the platform may gain commercial benefits from user behaviors on the platform such as clicks and purchases. As the platform may serve millions of users and can determine which contents to be recommended, it naturally has some advantages over individual user. Therefore, it would be crucial for the platform to make full use of its advantages in order to maximize the commercial benefits. One typical advantage of the platform is its information advantage, i.e., they may collect plenty of information over users and items for conducting better recommendation. Typical state-of-the-art recommendation systems (Covington et al., 2016; Guo et al., 2017; Ren et al., 2019; Zhou et al., 2019) always take these information into consideration including user profiles, item features and historical interactions between users and recommended items. It is worth noting that information over item features is always directly incorporated into the recommendation models without considering that the user may be with different levels of uncertainty over different item dimensions (which can be regarded as different hidden attributes describing different high-order features of the item). For instance, when buying a new coat on the platform, a user may be sure that the logistics is very fast as she (he) has bought clothes from the same online store before (i.e., the user is with low uncertainty over the logistics). But she (he) may be uncertain about the quality of the coat since it is of the brand that she (he) does not know much about (i.e., the user is with high uncertainty over the quality). Thus, it would be crucial for the platform to figure out whether it is possible to leverage the user uncertainty over different item dimensions to maximize the platform utility, and if yes, how?



Item dimensions: Typical state-of-the-art solutions for recommendation systems always encode each item as an embedding. The item dimensions refer to different dimensions of the item embedding, which can be explained as different high-order features.

