INSPIRE: Intent-aware Neural Sponsored Product Retrieval for E-commerce

• URL: http://arxiv.org/abs/2606.23889v1
• Date: Mon, 22 Jun 2026 19:39:26 GMT
• Title: INSPIRE: Intent-aware Neural Sponsored Product Retrieval for E-commerce
• Abstract summary: Walmart holds the largest share of the U.S. ecommerce grocery market, where food and beverage categories generate some of the highest search traffic and drive a substantial portion of sponsored search revenue.<n>Yet, understanding user intent in grocery search is inherently challenging.<n>We develop a weakly supervised intent learning pipeline, where a large language model serves as a teacher to generate structured intent annotations from product titles and descriptions.
• Score: 5.818822979956035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Walmart holds the largest share of the U.S. ecommerce grocery market, where food and beverage categories generate some of the highest search traffic and, consequently, drive a substantial portion of sponsored search revenue. At this scale, even small mismatches between user intent and retrieved products can lead to losses in both user engagement and monetization. Yet, understanding user intent in grocery search is inherently challenging. Queries are typically short, ambiguous, and highly diverse, often underspecifying critical preferences. From the advertisers perspective, many products are explicitly designed to target specific intents such as dietary preferences or size variants and must be surfaced at the right moment to be effective. Thus, we propose INSPIRE (Intent aware Neural Sponsored Product Retrieval for Ecommerce), an intent aware retrieval framework for sponsored search that leverages structured intent signals to better align user queries with relevant food and beverage products. INSPIRE represents intent as a set of structured, multi dimensional attributes derived from both user queries and product content, capturing explicit signals (e.g., brand, flavor) as well as implicit preferences (e.g., dietary constraints, cuisine types) that are often not directly expressed in queries. We develop a weakly supervised intent learning pipeline, where a large language model serves as a teacher to generate structured intent annotations from product titles and descriptions. We then distill these annotations by using them to finetune a lightweight student LLM model through LoRA based supervised finetuning that predicts intent attributes. We then introduce an intent augmented dense retrieval framework, where predicted intents are incorporated into query and product representations within a biencoder, enabling more precise matching between queries and sponsored products.

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