论文标题
基于距离的积极和未标记的学习
Distance-based Positive and Unlabeled Learning for Ranking
论文作者
论文摘要
学习排名 - 在一组监督项目方面，制作特定于查询的项目的排名列表 - 是一个普遍兴趣的问题。我们认为的设置是没有分析描述什么构成良好排名的设置。取而代之的是，我们有一个包含（目标项目，有趣的项目集）对的表示和监督信息的集合。我们在分析中，在模拟中进行了分析证明，在实际数据示例中，当监督与“这几个相似的项目相似”时，通过使用整数线性程序组合表示来进行排名是有效的。尽管此提名任务是相当笼统的，但对于特殊性，我们从图中的顶点提名的角度介绍了我们的方法论。本文描述的方法是模型不可知论。
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ranking is available. Instead, we have a collection of representations and supervisory information consisting of a (target item, interesting items set) pair. We demonstrate analytically, in simulation, and in real data examples that learning to rank via combining representations using an integer linear program is effective when the supervision is as light as "these few items are similar to your item of interest." While this nomination task is quite general, for specificity we present our methodology from the perspective of vertex nomination in graphs. The methodology described herein is model agnostic.
