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One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
Arxiv
0+阅读 · 6月14日
Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following
Arxiv
0+阅读 · 5月28日
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation
Arxiv
0+阅读 · 5月31日
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
Arxiv
0+阅读 · 5月29日
Effective Knowledge Transfer for Multi-Task Recommendation Models
Arxiv
0+阅读 · 5月7日
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
Arxiv
0+阅读 · 5月21日
Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Arxiv
0+阅读 · 5月28日
Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Arxiv
0+阅读 · 5月29日
A Logical-Rule Autoencoder for Interpretable Recommendations
Arxiv
0+阅读 · 4月5日
NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
Arxiv
0+阅读 · 4月8日
Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Arxiv
0+阅读 · 4月27日
CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation
Arxiv
0+阅读 · 4月8日
CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation
Arxiv
0+阅读 · 4月9日
FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost
Arxiv
0+阅读 · 4月27日
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
Arxiv
0+阅读 · 4月17日
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