TopKGAT: A Top-K Objective-Driven Architecture for Recommendation

2026-01-26

AI Summary

This work proposes TopKGAT, a novel recommendation architecture that explicitly aligns model design with the Top-K recommendation objective, addressing the limitation of conventional systems that lack such alignment and thus underperform on metrics like Precision@K and Recall@K. By integrating graph attention networks with differentiable Top-K approximation techniques, TopKGAT embeds an inductive bias directly oriented toward optimizing Top-K performance. Its forward computation is dynamically aligned with the gradient ascent direction of Precision@K, enabling end-to-end training that inherently prioritizes top-ranked recommendations. Extensive experiments on four benchmark datasets demonstrate that TopKGAT consistently outperforms state-of-the-art methods, achieving significant improvements in Top-K recommendation accuracy.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recommendation systems (RS) aim to retrieve the top-K items most relevant to users, with metrics such as Precision@K and Recall@K commonly used to assess effectiveness. The architecture of an RS model acts as an inductive bias, shaping the patterns the model is inclined to learn. In recent years, numerous recommendation architectures have emerged, spanning traditional matrix factorization, deep neural networks, and graph neural networks. However, their designs are often not explicitly aligned with the top-K objective, thereby limiting their effectiveness. To address this limitation, we propose TopKGAT, a novel recommendation architecture directly derived from a differentiable approximation of top-K metrics. The forward computation of a single TopKGAT layer is intrinsically aligned with the gradient ascent dynamics of the Precision@K metric, enabling the model to naturally improve top-K recommendation accuracy. Structurally, TopKGAT resembles a graph attention network and can be implemented efficiently. Extensive experiments on four benchmark datasets demonstrate that TopKGAT consistently outperforms state-of-the-art baselines. The code is available at https://github.com/StupidThree/TopKGAT.

Problem

• recommendation systems
• top-K recommendation
• Precision@K
• Recall@K
• inductive bias

Innovation

• Top-K Recommendation
• Differentiable Approximation
• Graph Attention Network
• Objective-Driven Architecture
• Precision@K Optimization