4 citations · 5 across the 14 of their papers we have counts for
8 papers · 1 filter
The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers
Zhe Xu, Prachi Agrawal, Kavosh Asadi +17
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains b…
Tokenizing Numerical and Embedding Features for LLM RecSys
Zhe Xu, Ankit Peshin, Chiyu Zhang +7
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilit…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
Dongqi Fu, Kaushik Rangadurai, Haiyu Lu +13
The increase in data volume, computational resources, and model parameters during training has led to the development of numerous large-scale industrial retrieval models for recomm…
GR2: Generative Reasoning Re-ranker
Mingfu Liang, Yufei Li, Jay Xu +20
Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work h…
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID
Carolina Zheng, Minhui Huang, Dmitrii Pedchenko +15
The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynam…
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking
Ilqar Ramazanli, Hamid Eghbalzadeh, Xiaoyi Liu +6
Perturbation-based regularization techniques address many challenges in industrial-scale large models, particularly with sparse labels, and emphasize consistency and invariance for…
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation
Kaushik Rangadurai, Siyang Yuan, Minhui Huang +12
Retrieval, the initial stage of a recommendation system, is tasked with down-selecting items from a pool of tens of millions of candidates to a few thousands. Embedding Based Retri…