Enhancing Dataset Search with Compact Data Snippets - SIGIR 2024
Association for Computing Machinery (ACM) via YouTube
NY State-Licensed Certificates in Design, Coding & AI — Online
Learn Generative AI, Prompt Engineering, and LLMs for Free
Overview
AI, Data Science & Cloud Certificates from Google, IBM & Meta — 40% Off
One plan covers every Professional Certificate on Coursera. 40% off Coursera Plus Annual.
Unlock All Certificates
Explore a 15-minute conference talk from SIGIR 2024 focused on enhancing dataset search through the use of compact data snippets. Presented by authors Qiaosheng Chen, Jiageng Chen, Xiao Zhou, and Gong Cheng, this domain-specific presentation delves into innovative techniques for improving the efficiency and effectiveness of dataset search algorithms. Learn about the challenges faced in dataset retrieval and discover how compact data snippets can be leveraged to provide more accurate and relevant search results. Gain insights into the latest advancements in information retrieval systems and their applications in managing and accessing large-scale datasets. This talk, organized by the Association for Computing Machinery (ACM), offers valuable knowledge for researchers, data scientists, and professionals working with big data and search technologies.
Syllabus
SIGIR 2024 T1.2 [fp] Enhancing Dataset Search with Compact Data Snippets
Taught by
Association for Computing Machinery (ACM)