{"ID":5676030,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T23:00:06.730503183Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01530","arxiv_id":"2607.01530","title":"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.","short_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 detecti...","url_abs":"https://arxiv.org/abs/2607.01530","url_pdf":"https://arxiv.org/pdf/2607.01530v1","authors":"[\"Rachith Aiyappa\",\"Ishita Khan\",\"Chester Palen-Michel\",\"Jayanth Yetukuri\",\"Samarth Agrawal\",\"Mehran Elyasi\",\"Shuang Zhou\"]","published":"2026-07-01T23:02:00Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\"]","methods":"[]","has_code":false}