14 citations · 23 across the 5 of their papers we have counts for
6 papers
Query Generation with Direct Preference Optimization for Document Expansion in E-commerce Search
Kaihao Li, Feng Liu, Juexin Lin +4
Doc2Query, a popular document expansion technique, leverages sequence-to-sequence models to generate relevant queries, effectively addressing the "vocabulary mismatch" problem in i…
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…
On the Practice of Scaling Search Conversion Rate Prediction
James Pak, Jyun-Yu Jiang, Fan Zhang +13
Scaling a Search Conversion Rate (CVR) prediction model, especially in high-traffic environments, presents a challenge: superior model quality needs to be balanced with strict cons…
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…
Doc2Token: Bridging Vocabulary Gap by Predicting Missing Tokens for E-commerce Search
Kaihao Li, Juexin Lin, Tony Lee
Addressing the "vocabulary mismatch" issue in information retrieval is a central challenge for e-commerce search engines, because product pages often miss important keywords that c…