QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation
arXiv:2606.05671v1 Announce Type: new Abstract: Query recommendation in e-commerce search aims to proactively suggest queries that match users' potential interests. However, existing methods mainly optimize query-level relevance, while neglecting whether the retrieved products align with users' downstream preferences. This mismatch often leads to high query click through rates (CTR) but low product conversion rates (CVR). To bridge this gap, we propose QueryAgent-R1, a memory-augmented agentic framework that improves end-to-end alignment via chain-of-retrieval optimization. Our QueryAgent-R1 g
The increasing sophistication of AI models and the critical need for improved e-commerce conversion rates are driving innovations in query recommendation.
This research directly addresses a significant inefficiency in e-commerce, aiming to align AI-driven search with actual user purchasing intent rather than just clicks.
Product retrieval and query generation in e-commerce search could become significantly more effective, moving beyond simple relevance to optimize for conversion.
- · E-commerce platforms
- · Online retailers
- · AI developers in e-commerce
- · Consumers
- · E-commerce platforms with basic search algorithms
- · Advertising models based purely on click-through rates
Improved e-commerce efficiency and higher conversion rates for online retailers.
Increased consumer satisfaction and potentially reduced ad spend for inefficient targeting.
Deeper integration of AI agents across various aspects of online retail, leading to more personalized and predictive shopping experiences.
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Read at arXiv cs.CL