This presentation details Shopee's approach to constructing a multilingual product knowledge graph, covering ontology modeling, data acquisition, fusion techniques, and practical applications, while discussing challenges, model architectures, and future directions for large‑scale e‑commerce AI systems.
This article describes how Meituan builds a multi‑level, multi‑dimensional retail product knowledge graph to support new‑retail scenarios, detailing its architecture, data acquisition challenges, labeling pipelines, attribute extraction methods, efficiency improvements, human‑machine collaboration, and downstream search and recommendation applications.
The paper describes Meituan’s retail product knowledge graph—a multi‑layered, multi‑modal system that structures billions of SKUs, attributes, and user insights using hierarchical categories, graph‑enhanced NER, semi‑supervised learning, and expert‑in‑the‑loop validation, enabling precise search, ranking, recommendation, and real‑time merchant optimization.
This article explains how to build and manage product attribute libraries, covering the definition of attributes, their classification, grouping strategies, the relationship between attributes and categories, and brand handling to streamline e‑commerce product management at scale.
This article explains how well‑designed product categories serve as the backbone of an e‑commerce platform, covering the concepts of backend and frontend categories, the construction of category trees, and various mapping strategies that help both users find items quickly and operators manage large inventories efficiently.
