4 citations · 4 across the 9 of their papers we have counts for
9 papers
TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding
Rohan Kumar, Steven Xu, Kyle MacDonald +4
Product catalogs underpin search, discovery, and recommendation in e-commerce, yet they are often attribute-sparse: the attributes shoppers and downstream systems rely on are eithe…
One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation
Steven Xu, Sanjyot Thete, Saathvik Dirisala +7
Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers, fragmenting behavioral evidence across merchants. Expert-def…
Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
Emmanuel Aboah Boateng, Kyle MacDonald, Amardeep Kumar +2
Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injection, cost, latency, and generat…
Joint Optimization of Relevance and Engagement in Multi-Task Ranking for E-Commerce with Efficient LLM Supervision
Luming Chen, Jiaqi Xi, Raghav Saboo +7
Optimizing industrial search ranking models solely for user engagement signals often introduces systematic biases, prioritizing popular or price-anchored items that may not satisfy…
Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
Alejandro Breen Herrera, Aayush Sheth, Steven G. Xu +8
Conversational shopping assistants (CSAs) represent a compelling application of agentic AI, but moving from prototype to production reveals two underexplored challenges: how to eva…
Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
Emmanuel Aboah Boateng, Kyle MacDonald, Akshad Viswanathan +1
Accurately mapping user queries to business categories is a fundamental Information Retrieval challenge for multi-category marketplaces, where context-sparse queries such as "Wildf…
Mine and Refine: Optimizing Graded Relevance in E-commerce Semantic Search Retrieval
Jiaqi Xi, Raghav Saboo, Luming Chen +8
Embedding-based retrieval (EBR) for large-scale e-commerce search faces three intertwined challenges: graded (non-binary) relevance where engagement signals are noisy and intent-va…
Learning Slab Classes to Alleviate Memory Holes in Memcached
Devang Jhabakh Jai, Sudeep Das
We consider the problem of memory holes in slab allocators, where an item entered into memory occupies more memory than it actually requires due to a difference between the nearest…
Challenges in Search on Streaming Services: Netflix Case Study
Sudarshan Lamkhede, Sudeep Das
We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search…