FC-GSSL improves graph SSL by generating high-frequency biased corrupted graphs via low-frequency contribution-based corruption, reconstructing low-frequency features in an autoencoder, and aligning multi-view representations to fuse frequency bands.
Lightgcl: Simple yet effective graph contrastive learning for recommendation
3 Pith papers cite this work. Polarity classification is still indexing.
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MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
SLSRec disentangles long- and short-term user interests via self-supervised contrastive learning and fuses them adaptively with attention, outperforming prior models on three public recommendation benchmarks.
citing papers explorer
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Graph self-supervised learning based on frequency corruption
FC-GSSL improves graph SSL by generating high-frequency biased corrupted graphs via low-frequency contribution-based corruption, reconstructing low-frequency features in an autoencoder, and aligning multi-view representations to fuse frequency bands.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests
SLSRec disentangles long- and short-term user interests via self-supervised contrastive learning and fuses them adaptively with attention, outperforming prior models on three public recommendation benchmarks.