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.

翻译：新闻推荐是一项具有挑战性的任务，需要根据每位用户的交互历史和偏好进行个性化处理。最近的研究利用预训练语言模型（PLM）的力量，通过主要分为三类的方法直接对新闻项目进行排序：点对点、点对点和列表式学习排序。虽然点对点方法提供了线性的推理复杂度，但它们未能捕捉到对排序任务更有效的项目间关键比较信息。相反，点对点和列表式方法擅长融入这些比较，但存在实际限制：点对点方法要么计算成本高昂，要么缺乏理论保证，而列表式方法在实践中往往表现不佳。在本文中，我们提出了一种基于PLM的新闻推荐新框架，以可扩展的方式整合了点对点相关性预测和点对点比较。我们对我们的框架进行了严格的理论分析，确立了我们的方法保证性能提升的条件。大量实验表明，我们的方法在MIND和Adressa新闻推荐数据集上优于最先进的方法。