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.
Kuairec: A fully-observed dataset and insights for evaluating recommender systems
6 Pith papers cite this work, alongside 131 external citations. Polarity classification is still indexing.
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Quotient DAGs enable forward-flow importance sampling and exact unordered slate propensities via Forward-DP for autoregressive slate loggers under set-sufficient interfaces.
UxSID models ultra-long user sequences with semantic-group shared interest memory using Semantic IDs and dual-level attention, achieving state-of-the-art performance and a 0.337% revenue lift in advertising A/B tests.
A self-attention-only aggregator over frozen visual embeddings, optionally guided by CLIP-title frame selection, improves short-video recommendation accuracy while cutting training cost.
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
citing papers explorer
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KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
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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Quotient DAGs for Off-Policy Evaluation:Forward-Flow Importance Sampling and Exact Slate Propensities
Quotient DAGs enable forward-flow importance sampling and exact unordered slate propensities via Forward-DP for autoregressive slate loggers under set-sufficient interfaces.
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UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
UxSID models ultra-long user sequences with semantic-group shared interest memory using Semantic IDs and dual-level attention, achieving state-of-the-art performance and a 0.337% revenue lift in advertising A/B tests.
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Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
A self-attention-only aggregator over frozen visual embeddings, optionally guided by CLIP-title frame selection, improves short-video recommendation accuracy while cutting training cost.
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Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
- A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods