14 citations · 24 across the 5 of their papers we have counts for
5 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…
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…
Enhancing Relevance of Embedding-based Retrieval at Walmart
Juexin Lin, Sachin Yadav, Feng Liu +8
Embedding-based neural retrieval (EBR) is an effective search retrieval method in product search for tackling the vocabulary gap between customer search queries and products. The i…
Relevance Filtering for Embedding-based Retrieval
Nicholas Rossi, Juexin Lin, Feng Liu +4
In embedding-based retrieval, Approximate Nearest Neighbor (ANN) search enables efficient retrieval of similar items from large-scale datasets. While maximizing recall of relevant…
Large Language Models for Relevance Judgment in Product Search
Navid Mehrdad, Hrushikesh Mohapatra, Mossaab Bagdouri +8
High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search, yet measuring relevance of items to queries is one of the most…