KuaiLive is the first publicly released real-time interactive dataset for live streaming recommendation, with logs from 23,772 users and 452,621 streamers over 21 days plus timestamps, multi-type interactions, and side features.
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36 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
VarLenRec learns variable-length semantic IDs for generative recommendation by allocating longer codes to tail items via popularity-weighted information budget allocation, hyperbolic residual quantization, and a differentiable soft length controller.
X-SYNTH synthesizes enterprise context from digital human attention using Digital Twin Signatures and seven attention filters, raising true lead rate from 9.5% to 61.9% while cutting false lead rate to 18.8%.
AsymRec decouples input and output representations in generative recommendation via multi-expert semantic projection and multi-faceted hierarchical quantization, outperforming prior models by 15.8% on average.
OSA improves LLM-based recommenders by anchoring ordinal preference levels as numeric tokens in the model's latent space to retain fine-grained strength information when fusing collaborative signals.
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.
AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
TokenFormer unifies multi-field and sequential recommendation modeling via bottom-full-top-sliding attention and non-linear interaction representations to avoid sequential collapse and deliver state-of-the-art performance.
FAERec fuses collaborative ID embeddings with LLM semantic embeddings using adaptive gating and dual-level alignment to enhance tail-item sequential recommendations.
The paper proposes an item-aware attention mechanism with intra-item and inter-item layers to let LLMs capture item-level collaborative relations instead of only token-level ones.
S²GR adds stepwise thinking tokens with contrastive supervision on codebook clusters to balance computational focus and ground reasoning paths in generative recommendation.
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
PA-Bridge bridges passive conversation starter recommendations with active user expressions via adversarial distribution alignment and semantic discretization, yielding 0.54% higher feature penetration in online tests.
FEDIN improves CTR prediction by using target-aware frequency filtering to isolate low-entropy periodic interest signals from high-entropy noise in user attention patterns.
Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
CAST improves sequential recommendation by modeling fine-grained semantic transitions and using LLM priors to capture true item complementarity, reporting up to 17.6% Recall and 16.0% NDCG gains over prior methods.
BDPL improves heterogeneous sequential recommendation by constructing behavior-aware subgraphs, aggregating via cascade GNN, and enhancing representations with preference-level contrastive learning before adaptive fusion for target behavior prediction.
RoTE is a multi-level rotary time embedding module that explicitly models time spans in sequential recommendation and improves NDCG@5 by up to 20.11% when added to standard backbones on public benchmarks.
LGCD creates pseudo-overlapping user data via LLM reasoning and uses conditional diffusion to generate target-domain user representations for inter-domain sequential recommendation without real overlapping users.
Sub-sequence splitting interferes with fair evaluation in sequential recommendation models and enhances performance only when paired with particular splitting, targeting, and loss function choices.
FAVE replaces multi-step flow generation with a learned global average velocity from a semantic anchor prior, delivering SOTA accuracy and roughly 10x faster inference on recommendation benchmarks.
FLAME condenses ensemble diversity into a single network via modular ensemble simulation and guided mutual learning during training, delivering ensemble-level performance with single-network inference speed on sequential recommendation tasks.
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.
citing papers explorer
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X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention
X-SYNTH synthesizes enterprise context from digital human attention using Digital Twin Signatures and seven attention filters, raising true lead rate from 9.5% to 61.9% while cutting false lead rate to 18.8%.