  Sequential recommendation systems aim to provide personalized recommendations
by analyzing dynamic preferences and dependencies within user behavior
sequences. Recently, Transformer models can effectively capture user
preferences. However, their quadratic computational complexity limits
recommendation performance on long interaction sequence data. Inspired by the
State Space Model (SSM)representative model, Mamba, which efficiently captures
user preferences in long interaction sequences with linear complexity, we find
that Mamba's recommendation effectiveness is limited in short interaction
sequences, with failing to recall items of actual interest to users and
exacerbating the data sparsity cold start problem. To address this issue, we
innovatively propose a new model, MaTrRec, which combines the strengths of
Mamba and Transformer. This model fully leverages Mamba's advantages in
handling long-term dependencies and Transformer's global attention advantages
in short-term dependencies, thereby enhances predictive capabilities on both
long and short interaction sequence datasets while balancing model efficiency.
Notably, our model significantly improves the data sparsity cold start problem,
with an improvement of up to 33% on the highly sparse Amazon Musical
Instruments dataset. We conducted extensive experimental evaluations on five
widely used public datasets. The experimental results show that our model
outperforms the current state-of-the-art sequential recommendation models on
all five datasets. The code is available at
https://github.com/Unintelligentmumu/MaTrRec.
