FAERec fuses collaborative ID embeddings with LLM semantic embeddings using adaptive gating and dual-level alignment to enhance tail-item sequential recommendations.
u rner, Tomke and Demarest, Damian and G \
3 Pith papers cite this work. Polarity classification is still indexing.
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Stochastic Attention applies random permutations to token sequences in sliding-window attention to achieve exponentially growing receptive fields and full coverage in logarithmic layers, outperforming standard SWA in language model pre-training and inference.
S4 models exhibit stable time-continuity unlike sensitive S6 models, with task continuity predicting performance and enabling temporal subsampling for better efficiency.
citing papers explorer
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Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
FAERec fuses collaborative ID embeddings with LLM semantic embeddings using adaptive gating and dual-level alignment to enhance tail-item sequential recommendations.
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Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention
Stochastic Attention applies random permutations to token sequences in sliding-window attention to achieve exponentially growing receptive fields and full coverage in logarithmic layers, outperforming standard SWA in language model pre-training and inference.
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Continuity Laws for Sequential Models
S4 models exhibit stable time-continuity unlike sensitive S6 models, with task continuity predicting performance and enabling temporal subsampling for better efficiency.