  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.
