Zeroshot Listwise Learning to Rank Algorithm for Recommendation

TL;DR

This paper introduces a zero-shot listwise learning to rank algorithm for recommendation systems, leveraging order statistics and power law distributions to improve accuracy and fairness.

Contribution

It presents a novel zero-shot listwise learning to rank method specifically designed for recommendation tasks, addressing the decline in ranking approaches.

Findings

The proposed algorithm is both accurate and fair in experiments.

It effectively utilizes order statistic approximation and power law distribution.

Outperforms existing methods in recommendation ranking tasks.

Abstract

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.