The UPRE framework introduces multi‑view domain prompts and unified representation enhancement to achieve zero‑shot domain adaptation for object detection, dramatically improving detection accuracy on unseen target domains across diverse visual scenarios.
This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.
This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.
In this talk, researcher Jiang Zhuoren from Zhejiang University reviews the current state of large language models applied to graph learning, discusses their roles across various graph scenarios, and outlines promising research directions for unified cross‑domain graph learning.
The article highlights ten representative Meituan technical papers accepted at ACM SIGIR 2022, spanning personalized opinion tagging, cross‑domain sentiment classification, dialogue summarization transfer, universal retrieval, CTR prediction, image behavior modeling, and topic segmentation, each summarized with abstracts and download links for researchers.
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.
