Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. The architecture of Rankformer is inspired by the gradient of the ranking objective, embodying a unique (graph) transformer architecture -- it leverages global information from all users and items to produce more informative representations and employs specific attention weights to guide the evolution of embeddings towards improved ranking performance. We further develop an acceleration algorithm for Rankformer, reducing its complexity to a linear level with respect to the number of positive instances. Extensive experimental results demonstrate that Rankformer outperforms state-of-the-art methods. The code is available at https://github.com/StupidThree/Rankformer.

翻译：推荐系统（RS）旨在为每位用户生成个性化的排序列表，并使用排序指标进行评估。尽管个性化排序是推荐系统的基本属性，但这一关键特性在模型架构设计中常被忽视。为解决此问题，我们提出了Rankformer——一种受排序目标启发的推荐模型。Rankformer的架构设计灵感来源于排序目标的梯度，其体现为一种独特的（图）Transformer架构：该模型利用来自所有用户和项目的全局信息以生成信息量更丰富的表征，并采用特定的注意力权重来引导嵌入向提升排序性能的方向演化。我们进一步为Rankformer开发了加速算法，将其计算复杂度降至与正例数量呈线性关系。大量实验结果表明，Rankformer的性能优于现有最先进方法。代码发布于https://github.com/StupidThree/Rankformer。