IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
Abstract
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.
Cite
@article{arxiv.2607.01530,
title = {IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search},
author = {Rachith Aiyappa and Ishita Khan and Chester Palen-Michel and Jayanth Yetukuri and Samarth Agrawal and Mehran Elyasi and Shuang Zhou},
journal= {arXiv preprint arXiv:2607.01530},
year = {2026}
}