DS-MLP achieves state-of-the-art CTR prediction on three benchmarks using a final vanilla MLP structure trained via knowledge distillation and two alignment strategies.
Masknet: Introducing feature-wise multi- plication to ctr ranking models by instance-guided mask.arXiv preprint arXiv:2102.07619, 2021
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UniRec bridges the expressive gap in generative recommendation by prefixing semantic ID sequences with structured attribute tokens, recovering explicit feature crossing and yielding +22.6% HR@50 gains plus online lifts in PVCTR, orders, and GMV.
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
Empirical scaling of backbone, embeddings, and data shows largely independent additive gains, enabling a deployed model with 2.5x data and 8x compute that delivers +2.6% CVR improvement with minimal latency change.
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Dual-Stream MLP is All You Need for CTR Prediction
DS-MLP achieves state-of-the-art CTR prediction on three benchmarks using a final vanilla MLP structure trained via knowledge distillation and two alignment strategies.
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UniRec: Bridging the Expressive Gap between Generative and Discriminative Recommendation via Chain-of-Attribute
UniRec bridges the expressive gap in generative recommendation by prefixing semantic ID sequences with structured attribute tokens, recovering explicit feature crossing and yielding +22.6% HR@50 gains plus online lifts in PVCTR, orders, and GMV.
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Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
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Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
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On the Practice of Scaling Search Conversion Rate Prediction
Empirical scaling of backbone, embeddings, and data shows largely independent additive gains, enabling a deployed model with 2.5x data and 8x compute that delivers +2.6% CVR improvement with minimal latency change.