Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. We present AutoPKG, a multi-agent Large Language Model (LLM) framework that automatically constructs a Product-attribute Knowledge Graph (PKG) from multimodal product content. AutoPKG induces product types and type-specific attribute keys on demand, extracts attribute values from text and images, and consolidates updates through a centralized decision agent that maintains a globally consistent canonical graph. We also propose an evaluation protocol for dynamic PKGs that measures type/key validity and consolidation quality, as well as edge-level accuracy for value assertions after canonicalization. On a large real-world marketplace catalog dataset from Lazada (Alibaba), AutoPKG achieves up to 0.953 Weighted Knowledge Efficiency (WKE) for product types, 0.724 WKE for attribute keys, and 0.531 edge-level F1 for multimodal value extraction. Across three public benchmarks, we improve edge-level exact-match F1 by 0.152 and yield a 0.208 precision gain on the attribute extraction application. Online A/B tests show that AutoPKG-derived attributes increase Gross Merchandise Value (GMV) in Badge (+3.81%), Search (+5.32%), and Recommendation (+7.89%), supporting AutoPKG’s practical value in production.

Narrative-driven recommendation queries are common in question-answering platforms, AI search engines, social forums, and some domain-specific vertical applications. Users typically submit free-form text requests for recommendations, e.g., “Any mind-bending thrillers like Shutter Island you’d recommend?” Such special queries have traditionally been addressed as generic QA task under the RAG paradigm. This work formally introduces narrative recommendation as a distinct task and contends that the RAG paradigm is inherently ill-suited for it, owing to information loss in LLMs when retrieving information from from multiple long and fragmented contexts, and limitations in ranking effectiveness. To overcome these limitations, we propose a novel retrieve-rank paradigm by theoretically demonstrating its superiority over RAG paradigm. Central to this new paradigm, we specially focus on the information retrieval stage and introduce Open-domain Candidate Generation (OCG)-Agent that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information. We validate effectiveness of new paradigm and OCG-Agent’s retrieve mechanism under real-world datasets from Reddit and corporate education-consulting scenarios. Further extensive ablation studies confirming the rationality of each OCG-Agent component.