Selective State-Space Models with Adaptive Collaborative Awareness for Sequential Recommendation

Sequential recommendation systems face challenges in integrating local sequential patterns with global collaborative information. While Transformers capture long-term dependencies through self-attention, they suffer from quadratic complexity. State-space models offer linear efficiency but are constrained by Markovian assumptions that limit their ability to model direct inter-item relationships. This paper addresses the expressiveness limitations of selective state-space models in capturing collaborative signals. We propose MCARec, which integrates selective state spaces with a dedicated collaborative awareness module. The key components include: (1) a lightweight attention mechanism that explicitly models item co-occurrence and transition patterns, enabling direct pairwise relationship modeling beyond the sequential bottleneck; (2) context-aware adaptive gating that dynamically balances sequential and collaborative features based on input context; (3) a lightweight architecture that enhances representational capacity while maintaining computational efficiency. On MovieLens-1M, a dataset characterized by dense user interactions, MCARec achieves improvements of 3.89% in HR@10, 5.52% in NDCG@10, and 6.97% in MRR@10 over Mamba4Rec, and 9.19%, 12.09%, and 8.45% respectively over SASRec (all p<0.001). Performance gains correlate with interaction density: substantial improvements on dense datasets diminish on sparser Amazon datasets (2–6% over SASRec in most metrics), while showing mixed results compared to Mamba4Rec on sparse datasets, suggesting that the collaborative awareness mechanism is most effective when sufficient co-occurrence signals are available. This work provides the first systematic analysis of how Markovian constraints in state-space models limit collaborative information utilization in recommendations. MCARec demonstrates that augmenting state-space models with explicit collaborative modeling significantly improves recommendation accuracy in dense interaction scenarios, offering a complementary approach to pure sequential or pure attention-based methods.