Multilabel ranking is a central task in machine learning with widespread applications to web search, news stories, recommender systems, etc. However, the most fundamental question of learnability in a multilabel ranking setting remains unanswered. In this paper, we characterize the learnability of multilabel ranking problems in both the batch and online settings for a large family of ranking losses. Along the way, we also give the first equivalence class of ranking losses based on learnability.

翻译：多标记排序是机器学习中的一个核心任务，广泛应用于网络搜索、新闻报道、推荐系统等领域。然而，在多标记排序设置中最基本的可学习性问题仍未解决。在本文中，我们表征了大量排名损失函数的多标记排序问题的可学习性，包括批处理和在线设置。同时，我们还给出了首个基于可学习性的损失排名等价类。