OneSug: A Unified End-to-End Generative Framework for E-commerce Query Suggestion (AAAI 2026)

Overview

OneSug is a research paper accepted to AAAI 2026 proposing a unified end-to-end generative framework for e-commerce query suggestion. It falls under the areas of Search Assistance and Recommender Engines.

• Source: https://dl.acm.org/doi/abs/10.1145/3773966.3777963
• Type: Academic paper

Background and Motivation

In large-scale search, recommendation, and personalization systems, information retrieval has long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline-based methods tend to split retrieval, ranking, and generation into separate stages, making it hard to meet the demands of the LLM era: natural language interaction, multi-hop reasoning, and up-to-date knowledge. OneSug is proposed against this backdrop, aiming to advance the theory and practice of unifying the query suggestion process.

Problem Scope

The paper addresses core scenarios including:

• Open-domain information access
• Enterprise knowledge retrieval
• Conversational search
• Semantic understanding in recommendation systems
• End-to-end architectures that combine external knowledge sources with generative models

Method Design Patterns

Work in this area typically follows a four-step pattern: problem formalization → model/system design → training/construction → inference pipeline.

1. Input and representation: encode queries, documents, and user context into dense or sparse representations, or structured prompts 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, chained or combined by task 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control

Evaluation Considerations

Typical benchmarks and metrics in this space include:

• Datasets: MS MARCO, BEIR, Natural Questions, domain-specific corpora, public recommendation datasets
• Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency, and token cost
• Baselines: BM25, dense retrieval, cross-encoder reranking, retrieval-free LLMs, commercial search APIs

> Note: Exact numerical results should be verified against the original paper PDF; this post is based on the abstract and public metadata.

Takeaways for Search / Rec / Personalization

1. Architecture: cascaded retrieval + rerank + generation remains mainstream, but agentic paradigms are making retrieval count and policy themselves learnable 2. Data: high-quality instruction data and click/session logs matter equally; synthetic data must guard against knowledge leakage and distribution shift 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human evaluation 4. Product: latency, cost, explainability, and safety are hard constraints for industrial deployment—not just academic benchmarks

Limitations and Future Directions

Likely limitations include experiment scale constrained by GPU budget, mismatch between benchmarks and real user distributions, unknown cross-lingual generalization from English-centric data, and safety risks of agentic systems on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.

Related Work

• Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (arXiv:2305.13808)
• Asking Clarifying Questions in Open-Domain Information-Seeking Conversations (DOI: 10.1145/3331184.3331265)
• DiAL: Diversity-aware listwise ranking for query auto-complete (EMNLP)
• Enhancing Discoverability in Enterprise Conversational Systems (arXiv:2412.10933)
• Evaluating auto-complete ranking for diversity and relevance (ECIR)
• Evaluation and Continual Improvement for an Enterprise AI Assistant (arXiv:2407.12003)

Glossary

| Term | Meaning | |------|---------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain, a ranking quality metric | | Agentic Search | Modeling search as sequential decision-making and tool use | | Gen-IR | Generative Information Retrieval |