Self-Attention Gated Linear Recurrent Units for Sequential Recommendation

Sequential recommendation is of utmost importance for personalized suggestions across various domains, and its effectiveness hinges critically on accurately capturing the intricate sequential dependencies among items. Two advanced architectures are widely adopted for this purpose: self-attention mechanisms (SAM), which excel at modelling global dependencies in sequences, and linear recurrent units (LRU), which specialize in capturing local sequential patterns. However, SAM inherently lacks the capability to convey sequential order information, while LRU may overemphasize item ordering. Crucially, while the complementary strengths of SAM and LRU architectures are promising, their direct integration risks overfitting in noisy real-world recommendation scenarios, as overly refined representations may predominantly capture training noise rather than generalizable patterns. To tackle this issue while preserving the advantages of both architectures, we design a dual-pronged solution: perturbation injection during recurrence enhances robustness, while Gibbs distribution-based uncertainty scaling reduces noise sensitivity. Integrating these strategies, we propose a self-attention gated linear recurrent units for sequential recommendation (SLSRec) model. Specifically, through a novel self-attention gated unit, SLSRec synergistically integrates SAM’s global modeling capability with LRU’s sequential processing strength. The model is optimized using a comprehensive objective function that combines cross-entropy recommendation loss, perturbation injection loss, and uncertainty-incorporated recommendation loss. Extensive experiments on real-world datasets demonstrate that SLSRec outperforms state-of-the-art methods, showing particular effectiveness in handling noisy interactions and recommending long-tail items

Algorithm design · Feature extraction · Advanced Bandit Algorithms Research · Face and Expression Recognition · Recommender Systems and Techniques