Versioned late materialization stores user histories once and reconstructs sequences just-in-time during training to cut redundancy and enable longer sequences in large-scale recommendation systems.
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Next Interest Flow models user intent as continuous evolutionary trajectories on a high-dimensional latent interest manifold with kinematic constraints, bidirectional alignment, and temporal causality mechanisms, yielding reported gains on industrial CTR data.
CCN applies contrastive learning on collaborative co-click/co-non-click signals to structure item representations for trigger-induced recommendations, showing 12.3% CTR and 12.7% order lift in an unseen Taobao scenario after training on a year of heterogeneous data.
AMEN aligns item-scene interactions via homogeneous spaces and a TSP mechanism to let all-domain movelines differentially affect CTR predictions, reporting +11.6% CTCVR lift in A/B tests.
citing papers explorer
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Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Versioned late materialization stores user histories once and reconstructs sequences just-in-time during training to cut redundancy and enable longer sequences in large-scale recommendation systems.
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Next Interest Flow: A Generative Pre-training Paradigm for Recommender Systems by Modeling All-domain Movelines
Next Interest Flow models user intent as continuous evolutionary trajectories on a high-dimensional latent interest manifold with kinematic constraints, bidirectional alignment, and temporal causality mechanisms, yielding reported gains on industrial CTR data.
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Beyond the Trigger: Learning Collaborative Context for Generalizable Trigger-Induced Recommendation
CCN applies contrastive learning on collaborative co-click/co-non-click signals to structure item representations for trigger-induced recommendations, showing 12.3% CTR and 12.7% order lift in an unseen Taobao scenario after training on a year of heterogeneous data.
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All-domain Moveline Evolution Network for Click-Through Rate Prediction
AMEN aligns item-scene interactions via homogeneous spaces and a TSP mechanism to let all-domain movelines differentially affect CTR predictions, reporting +11.6% CTCVR lift in A/B tests.