CONGA: Continual Neural Gated Architecture for Long-History Sequential Recommendation
Existing sequential recommendation models rely on absolute positional encodings and fixed context windows, producing two structural failure modes: out-of-distribution degradation on histories longer than the training window, and hard context limits that discard long-range interactions. We present CONGA (COntinual Neura...