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.
EditKG: Editing Knowledge Graph for Recommendation - Lecture 1
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.2 [fp] EditKG: Editing Knowledge Graph for Recommendation
Taught by
Association for Computing Machinery (ACM)
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