SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks
Researchers have introduced SinkRec, a novel architecture designed to improve long-sequence recommendation systems. This new model addresses the issue of "semantic state sink," where repetitive patterns can dominate the system's memory and bias its recommendations. SinkRec employs a hybrid approach, externalizing recurring patterns into a conditional memory and using a Temporal-Aware State-Relation Differential Gated DeltaNet to refine memory usage and focus on dynamic transitions. Experiments indicate that SinkRec is both effective and efficient for recommendation tasks. AI
IMPACT Introduces a new architecture to improve the efficiency and accuracy of recommendation systems dealing with long sequences.