News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pretrained language models (PLMs) to directly rank news items by using inference approaches that predominately fall into three categories: pointwise, pairwise, and listwise learning-to-rank. While pointwise methods offer linear inference complexity, they fail to capture crucial comparative information between items that is more effective for ranking tasks. Conversely, pairwise and listwise approaches excel at incorporating these comparisons but suffer from practical limitations: pairwise approaches are either computationally expensive or lack theoretical guarantees, and listwise methods often perform poorly in practice. In this paper, we propose a novel framework for PLM-based news recommendation that integrates both pointwise relevance prediction and pairwise comparisons in a scalable manner. We present a rigorous theoretical analysis of our framework, establishing conditions under which our approach guarantees improved performance. Extensive experiments show that our approach outperforms the state-of-the-art methods on the MIND and Adressa news recommendation datasets.

本研究解决了新闻推荐中个性化排序的挑战，尤其是如何在保留高效性和准确性的基础上进行有效比较。提出了一种新颖的框架，该框架将点式相关性预测与逐对比较相结合，能够在保证可扩展性的同时提升排序效果。实验结果表明，该方法在MIND和Adressa新闻推荐数据集上优于现有的最佳方法。

高效的逐点-逐对学习排序在新闻推荐中的应用