IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
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) ...
| Authors | Rachith Aiyappa, Ishita Khan, Chester Palen-Michel et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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