SUIN improves CTR prediction by augmenting target user sequences with similar users' behaviors via embedding-based retrieval, user-specific position encoding, and user-aware target attention.
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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.
DSAIN introduces situational features and tri-directional fusion to enhance behavior sequence modeling for CTR prediction, delivering 2.7% CTR, 2.62% CPM, and 2.16% GMV lifts in online A/B tests on the Meituan platform.
citing papers explorer
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Similar Users-Augmented Interest Network
SUIN improves CTR prediction by augmenting target user sequences with similar users' behaviors via embedding-based retrieval, user-specific position encoding, and user-aware target attention.
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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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Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
DSAIN introduces situational features and tri-directional fusion to enhance behavior sequence modeling for CTR prediction, delivering 2.7% CTR, 2.62% CPM, and 2.16% GMV lifts in online A/B tests on the Meituan platform.