无需训练数据，用统计规律实现精准公平的推荐排序
Zeroshot Listwise Learning to Rank Algorithm for Recommendation
- 利用顺序统计与幂律分布设计零样本列表级排序算法
- 在多个数据集上达到优于基线的准确率与公平性
- 适合缺乏标注数据的推荐场景，如冷启动或小样本
学习排序作为一种相对较少被关注的技术，其专业人才数量约为深度学习领域的六分之一。尽管学习排序在信息检索中广泛应用，但近年来作为推荐方法正逐渐衰落。本文充分利用顺序统计近似和幂律分布，设计了一种零样本列表级学习排序算法用于推荐，并在实验部分证明该方法兼具准确性和公平性。
原文摘要 · Abstract (English)
Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair.
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