Popularity-aware Distributionally Robust Optimization for Recommendation System

Abstract

Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.