MARS: Multi-Rate Aggregation of Recency Signals for Sequential Recommendation

Sequential recommendation requires both encoding the content of a user's history and deciding how that content should contribute to the next prediction. We study temporal readout as a modelling choice that can be controlled separately from sequence encoding. We introduce MARS (Multi-rate Aggregation of Recency Signals), a post-encoder operator that uses observed timestamps to construct K recency-weighted summaries. User-conditioned effective decay rates determine the temporal concentration of each head, and a context-dependent gate fuses the summaries with the encoder's final state. The same readout interface supports Transformer and Mamba backbones without changing downstream item scoring. Our analysis characterises a restricted kernel-approximation family and establishes how effective rates distinguish attention profiles and control their expected lag. In the reported results on four sparse benchmarks, the Transformer instantiation improves HR@10 by an average of 19.7% over the strongest content-only Transformer baseline on each dataset. On dense ML-1M, the Mamba instantiation exceeds SIGMA's HR@10 by 3.2%. Matched-backbone ablations show that gains depend on the encoder and dataset, while single-head variants remain competitive in several settings. These findings support explicit temporal readout as a modular means of introducing recency bias, with its value determined jointly by the encoded representation and the interaction history. Code is available at https://anonymous.4open.science/r/MARSS.