BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
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3 Pith papers cite this work, alongside 354 external citations. Polarity classification is still indexing.
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RCL adds similarity-based weak positive samples to supervised contrastive learning in sequential recommendation and reports an average 4.88% improvement over state-of-the-art methods across six datasets.
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.
citing papers explorer
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BIPCL: Bilateral Intent-Enhanced Sequential Recommendation via Embedding Perturbation Contrastive Learning
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
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Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
RCL adds similarity-based weak positive samples to supervised contrastive learning in sequential recommendation and reports an average 4.88% improvement over state-of-the-art methods across six datasets.
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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.