Research map / Large language models / Retrieval and knowledge
Building Task-Oriented Dialogue Systems for Online Shopping
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| CNN semantic matching instead of Naive Bayes | product category detection · off-line search log | ACC@1: 64.8% -> 67.3% | Helped |
| CNN semantic matching instead of BM25 | product category detection · off-line search log | ACC@1: 58.7% -> 67.3% | Helped |
| CNN semantic matching instead of Naive Bayes | product category detection · off-line search log | ACC@3: 85.0% -> 85.4% | No clear effect |
Closest papers by meaning
- Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models (NeurIPS 2024 · similarity 0.84)
- Mix-Ecom: Towards Mixed-Type E-Commerce Dialogues with Complex Domain Rules (ICLR 2026 · similarity 0.83)
- ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents (AAAI 2026 · similarity 0.82)
- Exploring Conversational Adaptability: Assessing the Proficiency of Large Language Models in Dynamic Alignment with Updated User Intent (AAAI 2025 · similarity 0.82)
- Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction (AAAI 2026 · similarity 0.81)
- Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training (ICLR 2025 · similarity 0.81)
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