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Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph

Autor: Zhao, Qian, Qian, Hao, Liu, Ziqi, Zhang, Gong-Duo, Gu, Lihong

Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions.

Externí odkaz: http://arxiv.org/abs/2402.13750

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Akademický článek

Postmortem Distribution and Postmortem Redistribution of Carbofuran-7-Phenyl Glucuronic Acid in Rabbits

Autor: WEN Yan, HAN Yu-zhe, GONG Duo, XIE Wen-kai, LÜ Chen-xi, MENG Yu-zhen, ZHANG Chao, WEI Zhi-wen, YUN Ke-ming

Publikováno v: Fayixue Zazhi, Vol 38, Iss 5, Pp 601-605 (2022)

ObjectiveTo establish a carbofuran intragastric administration death model in rabbits, and to observe the postmortem distribution and postmortem redistribution of carbofuran-7-phenyl glucuronic acid （Glu-7PH） in rabbits.MethodsThe postmortem dist

Externí odkaz: https://doaj.org/article/3662c252f5774d658c4a83318fd0961d

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Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate

Autor: Zhao, Shen-Yi, Zhang, Gong-Duo, Li, Ming-Wei, Li, Wu-Jun

Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regular

Externí odkaz: http://arxiv.org/abs/1803.05621

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Feature-Distributed SVRG for High-Dimensional Linear Classification

Autor: Zhang, Gong-Duo, Zhao, Shen-Yi, Gao, Hao, Li, Wu-Jun

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines.

Externí odkaz: http://arxiv.org/abs/1802.03604

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Lock-Free Optimization for Non-Convex Problems

Autor: Zhao, Shen-Yi, Zhang, Gong-Duo, Li, Wu-Jun

Stochastic gradient descent~(SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-scale problems, researchers have recently proposed several lock-free stra

Externí odkaz: http://arxiv.org/abs/1612.03441

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