6 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…