4 citations · 7 across the 4 of their papers we have counts for
4 papers
It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning
Runpeng Dai, Kaili Huang, Changsung Kang +1
Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and auction. Recent work increasi…
Generating Query-Relevant Document Summaries via Reinforcement Learning
Nitin Yadav, Changsung Kang, Hongwei Shang +1
E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predic…
Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
Hongwei Shang, Nguyen Vo, Nitin Yadav +6
Ensuring the products displayed in e-commerce search results are relevant to users queries is crucial for improving the user experience. With their advanced semantic understanding,…
Meta Learning to Rank for Sparsely Supervised Queries
Xuyang Wu, Ajit Puthenputhussery, Hongwei Shang +2
Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or c…