IAT compresses each historical interaction instance into a unified embedding token via temporal-order or user-order schemes, allowing standard sequence models to learn long-range preferences with better performance and transferability.
Hisac: Hierarchical sparse activation compression for ultra-long sequence modeling in recommenders
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SinkRec proposes a memory-conditioned architecture with TDGD to mitigate semantic state sink in linear attention for long-sequence recommendation.
citing papers explorer
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IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems
IAT compresses each historical interaction instance into a unified embedding token via temporal-order or user-order schemes, allowing standard sequence models to learn long-range preferences with better performance and transferability.
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SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks
SinkRec proposes a memory-conditioned architecture with TDGD to mitigate semantic state sink in linear attention for long-sequence recommendation.