mlsanityData to 14 Oct 2026

Semantic Retrieval at Walmart

arXiv 2412.04637 ↗First posted 5 Dec 2024cs.IR cs.AI cs.LGcited by 3

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Abstract (arXiv)

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 a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines traditional inverted index and embedding-based neural retrieval to better answer user tail queries. Our system significantly improved the relevance of the search engine, measured by both offline and online evaluations. The improvements were achieved through a combination of different approaches. We present a new technique to train the neural model at scale. and describe how the system was deployed in production with little impact on response time. We highlight multiple learnings and practical tricks that were used in the deployment of this system.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 44 references that are in the map. First posted on arXiv 5 Dec 2024.

Builds on

24 of 44 references.

References · 24

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12 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Modelscited by 4
  2. 2025Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rankcited by 0
  3. 2026JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modelingcited by 0