论文标题
使用稀缺数据排名的光谱方法
Spectral Methods for Ranking with Scarce Data
论文作者
论文摘要
给定许多项目的成对偏好，一个常见的任务是对所有项目进行排名。示例包括成对电影评分，纽约人卡通字幕比赛以及许多其他消费者偏好任务。这些设置的共同点有两个方面：数据稀缺（获得所有物品对的比较可能是昂贵的）和有关项目的其他功能信息（例如，电影类型，导演和演员）。在本文中，我们修改了一种受欢迎且研究良好的方法，排名汇总的rankcentrality可以说明很少的比较，并包含其他功能信息。该方法即使在不足的比较下也返回有意义的排名。使用基于扩散的方法，我们合并了特征信息，这些信息在实践中优于最先进的方法。我们还提供了各种抽样方案中的级别的样本复杂性。
Given a number of pairwise preferences of items, a common task is to rank all the items. Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many other consumer preferences tasks. What these settings have in common is two-fold: a scarcity of data (it may be costly to get comparisons for all the pairs of items) and additional feature information about the items (e.g., movie genre, director, and cast). In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information. This method returns meaningful rankings even under scarce comparisons. Using diffusion based methods, we incorporate feature information that outperforms state-of-the-art methods in practice. We also provide improved sample complexity for RankCentrality in a variety of sampling schemes.
