U-rank：基于效用的学习排序
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
摘要：
学习排序是许多现实世界信息系统中最重要的任务之一，其中目标是一些特定的效用，例如，点击和收入。然而，我们指出，现有的方法基于概率排序原则不一定达到最高的效用。为此，我们提出了一种新的排名框架，称为U-排名，直接优化的预期效用的排名列表。通过一个位置感知的深度点击率预测模型，我们解决了同时考虑查询级和项目级特征的注意偏差。由于项目特定的注意力偏差模型，预期效用的优化对应于项目位置二分图上的最大权重匹配。我们基于一个有效的Lambdaloss框架，这是支持理论和实证分析的优化这一目标。我们在三个基准数据集和两个专有数据集上对网络搜索和推荐系统进行了广泛的实验，其中展示了U-排名在最先进技术上的性能增益。此外，我们提出的U-排名已部署在一个大规模的商业推荐和生产基线的大幅度改善已被观察到在在线A/B测试。
英文摘要：
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue. However, we point out that existing methods based on probabilistic ranking principle do not necessarily achieve the highest utility. To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list. With a position-aware deep click-through rate prediction model, we address the attention bias considering both query-level and item-level features. Due to the item-specific attention bias modeling, the optimization for expected utility corresponds to a maximum weight matching on the item-position bipartite graph. We base the optimization of this objective in an efficient Lambdaloss framework, which is supported by both theoretical and empirical analysis. We conduct extensive experiments for both web search and recommender systems over three benchmark datasets and two proprietary datasets, where the performance gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.
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