Learning to Rank For Push Notifications Using Pairwise Expected Regret
This work addresses the challenge of improving push notification ranking for users on social networks, representing an incremental advancement in learning-to-rank methods for this specific domain.
The paper tackled the problem of ranking personalized mobile push notifications by introducing a novel ranking loss based on pairwise expected regret, and demonstrated that this method outperformed prior methods in both simulated and production experiments on a major social network.
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push notifications and discuss the unique challenges this presents compared to traditional ranking problems. To address these challenges, we introduce a novel ranking loss based on weighting the pairwise loss between candidates by the expected regret incurred for misordering the pair. We demonstrate that the proposed method can outperform prior methods both in a simulated environment and in a production experiment on a major social network.