Researchers proposed DSRec, a novel dual-interest cross-SSM model for sequential product recommendation. DSRec improves upon existing methods by capturing item polysemy and dynamic behavior across different temporal granularities. The model uses two SSM encoders: a full-sequence Mamba for long-term modeling and a time-modulated SSM for short-term intent. Experiments show DSRec outperforms other state-of-the-art methods on public benchmarks.