State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time recommendation. Furthermore, the self-attention operation is performed at a sequence-level, thereby making low-cost incremental inference challenging. Inspired by recent advances in efficient language modeling, we propose linear recurrent units for sequential recommendation (LRURec). Similar to recurrent neural networks, LRURec offers rapid inference and can achieve incremental inference on sequential inputs. By decomposing the linear recurrence operation and designing recursive parallelization in our framework, LRURec provides the additional benefits of reduced model size and parallelizable training. Moreover, we optimize the architecture of LRURec by implementing a series of modifications to address the lack of non-linearity and improve training dynamics. To validate the effectiveness of our proposed LRURec, we conduct extensive experiments on multiple real-world datasets and compare its performance against state-of-the-art sequential recommenders. Experimental results demonstrate the effectiveness of LRURec, which consistently outperforms baselines by a significant margin. Results also highlight the efficiency of LRURec with our parallelized training paradigm and fast inference on long sequences, showing its potential to further enhance user experience in sequential recommendation.

翻译：最先进的序列推荐严重依赖于基于自注意力的推荐模型。然而，此类模型计算成本高昂，且往往因速度过慢而难以满足实时推荐需求。此外，自注意力操作是在序列级别执行的，这使得低成本增量推理变得困难。受近期高效语言建模进展的启发，我们提出了用于序列推荐的线性循环单元（LRURec）。与循环神经网络类似，LRURec能够实现快速推理，并可对序列输入进行增量推理。通过分解线性循环运算并在框架中设计递归并行化，LRURec额外提供了模型规模缩减和训练可并行化的优势。此外，我们通过实施一系列改进措施来优化LRURec架构，以解决非线性缺失问题并改善训练动态。为验证所提出的LRURec的有效性，我们在多个真实世界数据集上进行了大量实验，并将其性能与最先进的序列推荐器进行比较。实验结果表明了LRURec的有效性，其持续以显著优势超越基线模型。结果还凸显了LRURec凭借并行化训练范式及长序列快速推理的优势，展现了其在序列推荐中进一步提升用户体验的潜力。