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TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

Recommender Systems Courses Artificial Intelligence Courses Data Mining Courses Machine Learning Courses Deep Learning Courses Neural Networks Courses Information Retrieval Courses Collaborative Filtering Courses Transformers Courses

Course Description

Overview

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Explore a cutting-edge approach to recommender systems in this 14-minute conference talk from SIGIR 2024. Delve into TransGNN, a novel framework that combines the strengths of Transformers and Graph Neural Networks to enhance recommendation accuracy and efficiency. Learn how authors Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Xi Zhang, Senzhang Wang, Feiran Huang, and Sunghun Kim leverage the collaborative power of these two powerful architectures to address challenges in modern recommendation systems. Gain insights into the potential applications and benefits of this innovative approach for improving user experiences and engagement in various digital platforms.

Syllabus

SIGIR 2024 T1.4 TransGNN: Harnessing the Collaborative Power of Transformers & Graph Neural Networks


Taught by

Association for Computing Machinery (ACM)

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