MiRec: Multi-scale Interest Decomposition and Mixing for Sequential Recommendation

Sequential recommendation predicts the next item a user will interact with by modeling the evolution of their interests from historical interactions. Multi-scale modeling captures user interests over different historical ranges but still faces two challenges. At the intra-scale level, stable and dynamic interests are entangled in holistic representations, making them difficult to model separately. At the cross-scale level, fusing entire representations does not adequately distinguish the mixing requirements of these two types of interests, limiting the targeted integration of complementary information. To address these challenges, we propose MiRec, a multi-scale interest decomposition and mixing model. Specifically, intra-scale interest decomposition extracts stable and dynamic interest representations through causal smoothing and residual computation. The cross-scale interest mixer then integrates representations of the same type from different scales through learnable subspace exchange. Finally, the updated representations are fused for next-item prediction. Experiments on four public datasets show that MiRec achieves relative improvements of 1.61%–5.81% in HR@10 and 2.72%–5.65% in NDCG@10 over the strongest baselines. Our code is available at https://anonymous.4open.science/r/MiRec-6B25/.