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.
Lightgcl: Simple yet effective graph contrastive learning for recommendation
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FC-GSSL is a graph self-supervised method that corrupts nodes/edges with high low-frequency contribution and reconstructs low-frequency/general targets, improving node and graph prediction on most tested benchmarks.
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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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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Frequency-Corrupt Based Graph Self-Supervised Learning
FC-GSSL is a graph self-supervised method that corrupts nodes/edges with high low-frequency contribution and reconstructs low-frequency/general targets, improving node and graph prediction on most tested benchmarks.
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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.