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[2602.12968] RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems
Summary
The RGAlign-Rec framework enhances proactive intent prediction in e-commerce chatbots by aligning latent query reasoning with ranking objectives, improving recommendation accuracy.
Why It Matters
This research addresses critical challenges in recommendation systems, particularly in e-commerce, where understanding user intent is essential for delivering relevant suggestions. By integrating a ranking-guided approach, the framework promises to improve user experience and service quality, making it significant for developers and businesses in the AI and e-commerce sectors.
Key Takeaways
- RGAlign-Rec integrates LLM-based reasoning with a Query-Enhanced ranking model.
- The framework addresses the semantic gap between user features and chatbot intents.
- Extensive experiments show significant improvements in prediction accuracy and error reduction.
- Online A/B testing validates the effectiveness of the ranking-aware alignment.
- This approach enhances proactive recommendations in real-world applications.
Computer Science > Information Retrieval arXiv:2602.12968 (cs) [Submitted on 13 Feb 2026] Title:RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems Authors:Junhua Liu, Yang Jihao, Cheng Chang, Kunrong LI, Bin Fu, Kwan Hui Lim View a PDF of the paper titled RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems, by Junhua Liu and 5 other authors View PDF HTML (experimental) Abstract:Proactive intent prediction is a critical capability in modern e-commerce chatbots, enabling "zero-query" recommendations by anticipating user needs from behavioral and contextual signals. However, existing industrial systems face two fundamental challenges: (1) the semantic gap between discrete user features and the semantic intents within the chatbot's Knowledge Base, and (2) the objective misalignment between general-purpose LLM outputs and task-specific ranking utilities. To address these issues, we propose RGAlign-Rec, a closed-loop alignment framework that integrates an LLM-based semantic reasoner with a Query-Enhanced (QE) ranking model. We also introduce Ranking-Guided Alignment (RGA), a multi-stage training paradigm that utilizes downstream ranking signals as feedback to refine the LLM's latent reasoning. Extensive experiments on a large-scale industrial dataset from Shopee demonstrate that RGAlign-Rec achieves a 0.12% gain in GAUC, leading to a significant 3.52% relative reduction in error rate, and a 0.56% impr...