New framework uses user behavior to clarify ambiguous e-commerce search queries

Researchers have developed IntentTune, a framework designed to improve e-commerce search by resolving ambiguous user queries. The system leverages user-specific behavioral data, such as search history and browsing activity, to infer latent intents like gender, age group, and product category. Experiments on real-world e-commerce data showed that user-specific signals, particularly prior search queries, were more effective than population-level demand patterns or static profile information for accurately determining user intent. AI

IMPACT This research could lead to more personalized and effective search experiences in e-commerce platforms.

RANK_REASON The cluster contains an academic paper detailing a new framework for improving e-commerce search. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

• e-commerce

• paper
• product
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →