# IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search

arXiv: https://arxiv.org/abs/2607.01530 · HTML: https://mlsanity.com/paper/2607.01530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants](https://mlsanity.com/paper/2506.09902/) (2025)
- [MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce](https://mlsanity.com/paper/2304.12636/) (2023)
- [Enhancing Relevance of Embedding-based Retrieval at Walmart](https://mlsanity.com/paper/2408.04884/) (2024)
- [Dataset of Natural Language Queries for E-Commerce](https://mlsanity.com/paper/2302.06355/) (2023)
- [Neural Search: Learning Query and Product Representations in Fashion E-commerce](https://mlsanity.com/paper/2107.08291/) (2021)
- [QueryNER: Segmentation of E-commerce Queries](https://mlsanity.com/paper/2405.09507/) (2024)
- [Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search](https://mlsanity.com/paper/2507.22213/) (2025)

## Abstract (arXiv)

Understanding user intent is fundamental to delivering relevant search results in e-commerce. However, substantial fraction of real-world queries are under-specified (e.g., "watch" or "shirt"), lacking explicit attributes such as gender or age group. This ambiguity poses a significant challenge for query intent detection models in e-commerce search systems, which must accurately infer latent user intent (e.g., age, gender) to support effective downstream retrieval. We introduce IntentTune, a framework for resolving ambiguous or under-specified query intents by leveraging either (1) user-specific behavioral signals including search history, browsing activity, and profile attributes or (2) population-level demand patterns aggregated across all users. Through experiments on real-world e-commerce data, we first demonstrate that population-level demand patterns alone are insufficient to reliably infer intent in under-specified queries. We then demonstrate that user-specific behavioral signals -- particularly prior search queries -- outperform both population-level statistics and static profile information for inferring gender, age group, product category, and size intent from underspecified queries.