Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models generally fail to achieve the three golden principles for sequential recommendation simultaneously, i.e., training efficiency, low-cost inference, and strong performance. To this end, we propose RecBLR, an Efficient Sequential Recommendation Model based on Behavior-Dependent Linear Recurrent Units to accomplish the impossible triangle of the three principles. By incorporating gating mechanisms and behavior-dependent designs into linear recurrent units, our model significantly enhances user behavior modeling and recommendation performance. Furthermore, we unlock the parallelizable training as well as inference efficiency for our model by designing a hardware-aware scanning acceleration algorithm with a customized CUDA kernel. Extensive experiments on real-world datasets with varying lengths of user behavior sequences demonstrate RecBLR's remarkable effectiveness in simultaneously achieving all three golden principles - strong recommendation performance, training efficiency, and low-cost inference, while exhibiting excellent scalability to datasets with long user interaction histories.

翻译：序列推荐系统旨在通过使用RNN和注意力等各类算子对用户行为进行建模，以预测用户的下一交互行为。然而，现有模型通常难以同时满足序列推荐的三大黄金准则：训练高效、推理低成本与强性能表现。为此，我们提出RecBLR——一种基于行为依赖线性循环单元的高效序列推荐模型，以达成这三项准则的"不可能三角"。通过将门控机制与行为依赖设计融入线性循环单元，我们的模型显著增强了用户行为建模能力与推荐性能。此外，我们通过设计结合定制CUDA内核的硬件感知扫描加速算法，实现了模型的可并行化训练与高效推理。在具有不同用户行为序列长度的真实数据集上进行的大量实验表明，RecBLR在同时达成所有三项黄金准则——强大的推荐性能、训练高效性与低成本推理——方面表现出显著有效性，并对具有长用户交互历史的数据集展现出优异的可扩展性。