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EditKG: Editing Knowledge Graph for Recommendation - Lecture 1

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

Tags

Knowledge Graphs Courses Artificial Intelligence Courses Data Mining Courses Machine Learning Courses Graph Theory Courses Reasoning Courses Information Retrieval Courses Recommendation Systems Courses

Course Description

Overview

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Explore a 14-minute conference talk from SIGIR 2024 focused on EditKG, an innovative approach to editing knowledge graphs for recommendation systems. Delve into the research presented by authors Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, and Chenghu Zhou as they discuss their findings in the field of Reasoning & Knowledge Graphs. Gain insights into how EditKG can potentially enhance recommendation algorithms by modifying knowledge graph structures. Learn about the methodology, challenges, and potential applications of this cutting-edge technique in the realm of information retrieval and recommendation systems.

Syllabus

SIGIR 2024 M1.2 [fp] EditKG: Editing Knowledge Graph for Recommendation


Taught by

Association for Computing Machinery (ACM)

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