Unified Supervision For Walmart's Sponsored Search Retrieval Via Joint Semantic Relevance And Behavioral Engagement Modeling

Abstract

Modern search systems rely on a fast first stage retriever to fetch relevant items from a massive catalog of items. Deployed search systems often use user engagement signals to supervise bi-encoder retriever training at scale, because these signals are continuously logged from real traffic and require no additional annotation effort. However, engagement is an imperfect proxy for semantic relevance. Items may receive interactions due to popularity, promotion, attractive visuals, titles, or price, despite weak query-item relevance. These limitations are further accentuated in Walmart's e-commerce sponsored search. User engagement on ad items is often structurally sparse because the frequency with which an ad is shown depends on factors beyond relevance such as whether the advertiser is currently running that ad, the outcome of the auction for available ad slots, bid competitiveness, and advertiser budget. Thus, even highly relevant query ad pairs can have limited engagement signals simpl

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydesai2026unified

Related papers

• Enhancing Relevance Of Embedding-based Retrieval At Walmart (2024)7.16
• Mine And Refine: Optimizing Graded Relevance In E-commerce Search Retrieval (2026)0.00
• Que2engage: Embedding-based Retrieval For Relevant And Engaging Products At Facebook Marketplace (2023)6.34
• Multimodal Semantic Retrieval For Product Search (2025)3.58
• Semantic-enhanced Modality-asymmetric Retrieval For Online E-commerce Search (2025)0.00
• Uni-retriever: Towards Learning The Unified Embedding Based Retriever In Bing Sponsored Search (2022)9.92
• Unified Embedding Based Personalized Retrieval In Etsy Search (2023)2.26
• MRSE: An Efficient Multi-modality Retrieval System For Large Scale E-commerce (2024)0.00