11 citations · 22 across the 8 of their papers we have counts for
9 papers
Scaling and Stabilizing Large-Scale Embedding-Based Retrieval
Zhen Yang, Juexin Lin, Hongwei Shang +8
Embedding-based retrieval (EBR) is foundational to large-scale e-commerce search, yet its effectiveness is often constrained by the quality of training signals and the representati…
Submodular Evaluation Subset Selection in Automatic Prompt Optimization
Jinming Nian, Zhiyuan Peng, Hongwei Shang +2
Automatic prompt optimization reduces manual prompt engineering, but relies on task performance measured on a small, often randomly sampled evaluation subset as its main source of…
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…
Classifying Emails into Human vs Machine Category
Changsung Kang, Hongwei Shang, Jean-Marc Langlois
It is an essential product requirement of Yahoo Mail to distinguish between personal and machine-generated emails. The old production classifier in Yahoo Mail was based on a simple…
A Framework for Network AB Testing
Bai Jiang, Xiaolin Shi, Hongwei Shang +2
A/B testing, also known as controlled experiment, bucket testing or splitting testing, has been widely used for evaluating a new feature, service or product in the data-driven deci…
A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes
Hongwei Shang, Jun Yan, Xuebin Zhang
In modeling spatial extremes, the dependence structure is classically inferred by assuming that block maxima derive from max-stable processes. Weather stations provide daily record…
A class of goodness-of-fit tests for spatial extremes models based on max-stable processes
Ivan Kojadinovic, Hongwei Shang, Jun Yan
Parametric max-stable processes are increasingly used to model spatial extremes. Starting from the fact that the dependence structure of a max-stable process is completely characte…