MLSA4Rec: Mamba Combined with Low-Rank Decomposed Self-Attention for Sequential Recommendation

In applications such as e-commerce, online education, and streaming services, sequential recommendation systems play a critical role. Despite the excellent performance of self-attention-based sequential recommendation models in capturing dependencies between items in user interaction history, their quadratic complexity and lack of structural bias limit their applicability. Recently, some works have replaced the self-attention module in sequential recommenders with Mamba, which has linear complexity and structural bias. However, these works have not noted the complementarity between the two approaches. To address this issue, this paper proposes a new hybrid recommendation framework, Mamba combined with Low-Rank decomposed Self-Attention for Sequential Recommendation (MLSA4Rec), whose complexity is linear with respect to the length of the user's historical interaction sequence. Specifically, MLSA4Rec designs an efficient Mamba-LSA interaction module. This module introduces a low-rank decomposed self-attention (LSA) module with linear complexity and injects structural bias into it through Mamba. The LSA module analyzes user preferences from a different perspective and dynamically guides Mamba to focus on important information in user historical interactions through a gated information transmission mechanism. Finally, MLSA4Rec combines user preference information refined by the Mamba and LSA modules to accurately predict the user's next possible interaction. To our knowledge, this is the first study to combine Mamba and self-attention in sequential recommendation systems. Experimental results show that MLSA4Rec outperforms existing self-attention and Mamba-based sequential recommendation models in recommendation accuracy on three real-world datasets, demonstrating the great potential of Mamba and self-attention working together.