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This talk explains how Meituan builds and leverages a massive lifestyle-domain knowledge graph to improve LBS recommendation, covering explicit and implicit graph applications, challenges such as explainability and data sparsity, and advanced models like dual‑memory networks and cross‑domain learning.
This article presents Meituan's large‑scale knowledge graph, its integration into location‑based recommendation, the challenges of explainability, domain diversity, data sparsity and spatiotemporal complexity, and describes a dual‑memory neural network and cross‑domain learning approach that improve recall, ranking and recommendation fairness.
This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.