Rankformer用排名目标设计图注意力模型，提升推荐排序效果。
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
- 基于排名目标的梯度设计图注意力架构，融合全局用户物品信息。
- 在多个数据集上超越现有方法，尤其在高阶排名指标上表现突出。
- 适合关注推荐系统排序性能优化的研究者与工程师。
推荐系统旨在为每位用户生成个性化的排序列表，并以排序指标进行评估。尽管个性化排序是推荐系统的核心，但现有模型架构常忽视这一关键特性。为此，我们提出Rankformer，一种受排名目标启发的推荐模型。其架构灵感来自排名目标的梯度，采用独特的（图）变换器结构：利用所有用户和物品的全局信息生成更丰富的表示，并通过特定注意力权重引导嵌入演化以提升排序性能。我们还设计了一种加速算法，使模型复杂度随正例数量线性增长。大量实验表明，Rankformer显著优于现有先进方法。代码已公开于 https://github.com/StupidThree/Rankformer。
原文摘要 · Abstract (English)
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.
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