Longer History or the Right History? Candidate-Focal History Compression for Long-Sequence Recommendation

Long-sequence recommendation seeks to improve ranking by leveraging longer user histories, under the premise that more historical interactions provide richer evidence of user preference. However, more history is not always better: historical evidence is often redundant, and, crucially, what is useful depends on the candidate being ranked. This observation motivates us to reframe long-sequence recommendation from blindly processing longer histories to *candidate-focal history compression*: preserving the right historical evidence for each candidate. This reframing raises two coupled challenges: a shared compressed history may discard candidate-specific evidence (**C1**), while independently constructing a history for every candidate multiplies redundant computation (**C2**). We develop a **quality–write–state tradeoff framework** to study this quality–efficiency tradeoff, which characterizes the first-order contribution of each recurrent write to the ranking objective, and then quantifies when candidates can share sparse histories with bounded error. Building on this theoretical frame, we introduce **DeltaRec**, which distills history contributions into a lightweight selector to guide both candidate-focal history-event writing and utility-based state sharing. Across three datasets and four backbones, we show that DeltaRec (1) improves ranking metrics by up to $71.0$ ($20.1$ on average) across all datasets over the corresponding backbones; (2) achieves a $17.48\times$ P50 prefill speedup over per-candidate state construction; and (3) extends the empirical quality–efficiency Pareto frontier compared to other baselines, achieving higher ranking quality at lower prefill cost than full-history execution. Together, these results support selecting and sharing the right history rather than blindly extending it. We release our code base at https://anonymous.4open.science/r/delta-rec-DB35.