AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.IR 4representative citing papers
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
Intermediate decoder hidden states from frozen LVLMs fused with ID embeddings outperform caption representations and deliver state-of-the-art micro-video recommendation performance on two real-world benchmarks.
citing papers explorer
-
Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation
AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
-
Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
-
WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
-
Frozen LVLMs for Micro-Video Recommendation: A Systematic Study of Feature Extraction and Fusion
Intermediate decoder hidden states from frozen LVLMs fused with ID embeddings outperform caption representations and deliver state-of-the-art micro-video recommendation performance on two real-world benchmarks.