LLM4AIGQ: LLM-based AI Guidance Query Generation Framework for Multi Interest Mining

• URL: http://arxiv.org/abs/2609.03674v1
• Date: Thu, 03 Sep 2026 11:13:01 GMT
• Title: LLM4AIGQ: LLM-based AI Guidance Query Generation Framework for Multi Interest Mining
• Abstract summary: We propose an LLM-based solution for generating AI guidance queries tailored to users' multi-interests.<n>This approach segments user interests by integrating user profiles and historical interaction sequences, infers specific consumption intents for each sub-interest.<n>Our model achieves robust performance in both offline evaluations and online A/B tests.
• Score: 4.054665499206523
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Guidance queries stimulate user consumption by extracting preferences to provide search queries with guidance value, playing a crucial role in the e-commerce field. Traditional AI-generated queries (AIGQ) generation primarily relies on a two-stage "Query-to-AI-Generated-Query" (Q2AIGQ) association paradigm, first recalling user primary search queries from user profiles, historical behavior sequences, item-side information, and the current query through multi-path retrieval, then generalizing AIGQ via rule-based methods. This approach suffers from semantic drift due to information cascade loss; additionally, primary search query derivation heavily depends on "user-item" co-occurrence relationships, lacking exploration of user multi-interests, resulting in guidance queries with low value and mismatched purchase intent. To address the expressive limitations of traditional co-occurrence-based retrieval, we propose LLM4AIGQ, an LLM-based solution for generating AI guidance queries tailored to users' multi-interests. This approach segments user interests by integrating user profiles and historical interaction sequences, infers specific consumption intents for each sub-interest, and subsequently generates corresponding AIGQ. In terms of model training, we employ a post-training pipeline comprising Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and Direct Preference Optimization (DPO) to enhance the model's capability in generating AIGQ. We also introduce a multi-level reward design to satisfy the requirements of multi-objective optimization and long-chain reasoning in practical applications. Regarding deployment, we adopt a nearline-generation and online-read architecture to meet latency constraints. Extensive experimental analyses demonstrate that our model achieves robust performance in both offline evaluations and online A/B tests.

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