论文标题
学习使用成对预期遗憾的推送通知排名
Learning to Rank For Push Notifications Using Pairwise Expected Regret
论文作者
论文摘要
列表的排名损失已在推荐系统中得到广泛研究。但是，内容消费的新范例提出了排名方法的新挑战。在这项工作中，我们对学习的分析有助于分析个性化移动推动通知，并讨论与传统排名问题相比，该挑战所带来的独特挑战。为了应对这些挑战，我们基于对候选人之间的成对损失的重量损失而引起的一项新排名损失。我们证明，在模拟环境和主要社交网络上的生产实验中，所提出的方法都可以超越先验方法。
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.
