6 papers · 1 filter
Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations
Benyu Zhang, Qiang Zhang, Rui Li +3
First-stage recommenders in multi-stage systems produce a ranked candidate list from which a limited prefix is forwarded to downstream rankers. Because each forwarded item must be…
Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval
Ivan Ji, Liuyi Hu, Harrison +6
The two-tower model is widely used in the retrieval stage of large-scale recommendation systems, where training typically relies on in-batch and/or out-of-batch negative sampling.…
Bending the Scaling Law Curve in Large-Scale Recommendation Systems
Qin Ding, Kevin Course, Linjian Ma +19
Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…
Efficient Sequential Recommendation for Long Term User Interest Via Personalization
Qiang Zhang, Hanchao Yu, Ivan Ji +14
Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for seque…
Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
Dai Li, Kevin Course, Wei Li +13
Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challen…