19 citations · 55 across the 12 of their papers we have counts for
13 papers
Overview of the TREC 2025 Product Search and Recommendation Track
Dean E. Alvarez, Surya Kallumadi, Daniel Campos +4
In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimagina…
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…
Semantic Retrieval at Walmart
Alessandro Magnani, Feng Liu, Suthee Chaidaroon +8
In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have…
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…
Overview of the TREC 2023 Product Product Search Track
Daniel Campos, Surya Kallumadi, Corby Rosset +2
This is the first year of the TREC Product search track. The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi…
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
Daniel Campos, Alessandro Magnani, ChengXiang Zhai
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymm…
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
Daniel Campos, ChengXiang Zhai, Alessandro Magnani
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document rank…
A Multi-task Learning Framework for Product Ranking with BERT
Xuyang Wu, Alessandro Magnani, Suthee Chaidaroon +3
Product ranking is a crucial component for many e-commerce services. One of the major challenges in product search is the vocabulary mismatch between query and products, which may…
Image Matters: Scalable Detection of Offensive and Non-Compliant Content / Logo in Product Images
Shreyansh Gandhi, Samrat Kokkula, Abon Chaudhuri +6
In e-commerce, product content, especially product images have a significant influence on a customer's journey from product discovery to evaluation and finally, purchase decision.…
A Smart System for Selection of Optimal Product Images in E-Commerce
Abon Chaudhuri, Paolo Messina, Samrat Kokkula +5
In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product image…
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
Tom Zahavy, Alessandro Magnani, Abhinandan Krishnan +1
Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of…