AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation

• URL: http://arxiv.org/abs/2603.19710v1
• Date: Fri, 20 Mar 2026 07:27:59 GMT
• Title: AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
• Abstract summary: Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery.<n>To overcome these challenges, we propose AIGQ, the first end-to-end generative framework for HintQ scenario.
• Score: 9.218475190088734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow semantics, poor cold-start performance and low serendipity due to reliance on ID-based matching and co-click heuristics. To overcome these challenges, we propose AIGQ (AI-Generated Query architecture), the first end-to-end generative framework for HintQ scenario. AIGQ is built upon three core innovations spanning training paradigm, policy optimization and deployment architecture. First, we propose Interest-Aware List Supervised Fine-Tuning (IL-SFT), a list-level supervised learning approach that constructs training samples through session-aware behavior aggregation and interest-guided re-ranking strategy to faithfully model nuanced user intent. Accordingly, we design Interest-aware List Group Relative Policy Optimization (IL-GRPO), a novel policy gradient algorithm with a dual-component reward mechanism that jointly optimizes individual query relevance and global list properties, enhanced by a model-based reward from the online click-through rate (CTR) ranking model. To deploy under strict real-time and low-latency requirements, we further develop a hybrid offline-online architecture comprising AIGQ-Direct for nearline personalized user-to-query generation and AIGQ-Think, a reasoning-enhanced variant that produces trigger-to-query mappings to enrich interest diversity. Extensive offline evaluations and large-scale online A/B experiments on Taobao demonstrate that AIGQ consistently delivers substantial improvements in key business metrics across platform effectiveness and user engagement.

Related papers

• LLM4AIGQ: LLM-based AI Guidance Query Generation Framework for Multi Interest Mining [4.054665499206523]
  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.
  arXiv  Detail & Related papers  (2026-09-03T11:13:01Z)
• HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization [19.90781293176099]
  HMACE is a Heterogeneous Multi-Agent Collaborative Evolution framework.<n>It decomposes each evolutionary generation into an autonomous, role-specialized loop with four coordinated agents.<n>It guides the search toward diverse and promising behaviors while avoiding redundant evaluations.
  arXiv  Detail & Related papers  (2026-05-08T04:02:28Z)
• CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search [51.911048955965136]
  CoSearch is a framework that jointly trains a multi-step reasoning agent and a generative document ranking model.<n>Our results show that joint training of the reasoning agent and retrieval system is both feasible and strongly performant.
  arXiv  Detail & Related papers  (2026-04-19T17:48:17Z)
• RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation [36.297513746770456]
  We propose RankGR, a Generative Retrieval method that incorporates listwise direct preference optimization for recommendation.<n>In IAP, we incorporate a novel listwise direct preference optimization strategy into GR, thus facilitating a more comprehensive understanding of the hierarchical user preferences.<n>We implement several practical improvements in training and deployment, ultimately achieving a real-time system capable of handling nearly ten thousand requests per second.
  arXiv  Detail & Related papers  (2026-02-09T12:13:43Z)
• Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment [8.610245271469267]
  Query Auto-Completion (QAC) suggests query completions as users type, helping them articulate intent and reach results more efficiently.<n>Traditional retrieve-and-rank pipelines have limited long-tail coverage and require extensive feature engineering.<n>We present a unified framework that reformulates QAC as end-to-end list generation through Retrieval-Augmented Generation (RAG) and multi-objective Direct Preference Optimization (DPO)
  arXiv  Detail & Related papers  (2026-02-01T05:15:07Z)
• GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation [38.48999566011862]
  We propose GPR (Generative Pre-trained Recommender), a one-model framework that redefines advertising recommendation as an end-to-end generative task.<n>We introduce three key innovations spanning unified representation, network architecture, and training strategy.<n>GPR has been fully deployed in the Tencent Weixin Channels advertising system, delivering significant improvements in key business metrics.
  arXiv  Detail & Related papers  (2025-11-13T09:50:53Z)
• Rethinking On-policy Optimization for Query Augmentation [49.87723664806526]
  We present the first systematic comparison of prompting-based and RL-based query augmentation across diverse benchmarks.<n>We introduce a novel hybrid method, On-policy Pseudo-document Query Expansion (OPQE), which learns to generate a pseudo-document that maximizes retrieval performance.
  arXiv  Detail & Related papers  (2025-10-20T04:16:28Z)
• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data.<n>Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates.<n>We propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering [57.94658176442027]
  We present RnG-KBQA, a Rank-and-Generate approach for KBQA. We achieve new state-of-the-art results on GrailQA and WebQSP datasets.
  arXiv  Detail & Related papers  (2021-09-17T17:58:28Z)
• AutoBERT-Zero: Evolving BERT Backbone from Scratch [94.89102524181986]
  We propose an Operation-Priority Neural Architecture Search (OP-NAS) algorithm to automatically search for promising hybrid backbone architectures. We optimize both the search algorithm and evaluation of candidate models to boost the efficiency of our proposed OP-NAS. Experiments show that the searched architecture (named AutoBERT-Zero) significantly outperforms BERT and its variants of different model capacities in various downstream tasks.
  arXiv  Detail & Related papers  (2021-07-15T16:46:01Z)
• CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking [97.84109669027225]
  We introduce an improved proposal refinement module, Cascaded Regression-Align- Classification (CRAC) CRAC yields new state-of-the-art performances on many benchmarks. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors.
  arXiv  Detail & Related papers  (2020-11-25T02:18:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.