DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
Hamilton, and Jure Leskovec
7 Pith papers cite this work. Polarity classification is still indexing.
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A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
TextBridgeGNN pre-trains a graph recommender across domains and uses text-similarity edges to move ID-based knowledge into a new domain, improving cross-domain, multi-domain, and zero-shot recommendations.
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.
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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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DREAM: Dynamic Refinement of Early Assignment Mappings
DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
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Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains a graph recommender across domains and uses text-similarity edges to move ID-based knowledge into a new domain, improving cross-domain, multi-domain, and zero-shot recommendations.
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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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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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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.