SIGKDD 2026
KDD is the premier Data Science and AI conference, hosting both a Research and an Applied Data Science Track. The conference will take place from August 9 to 13, 2026, in Jeju, Korea.
重要信息
CCF推荐：A（数据库／数据挖掘／内容检索）
录用率：18.4%（365/1988，2025年）
时间地点：2026年8月9日-济州·韩国
截稿时间：2026年2月8日
Call for Papers
Foundations of Knowledge Discovery and Data Science. Submissions are invited to discuss core models, algorithms, and theoretical insights for knowledge discovery.
Modern AI and Big Data. Submissions are invited to elaborate on the intersection of AI and massive data repositories.
Trustworthy and Responsible Data Science. Submissions are invited to feature techniques and frameworks that ensure responsible data use, management, and analysis.
Systems for Data Science and Scalable AI. Submissions that detail new architectures, systems, and infrastructures for large-scale data analysis and machine learning (e.g., distributed computing, federated learning, cloud-based systems) are invited.
Data Science Applications. Submissions are invited for innovative data science and artificial intelligence (AI) applications.
Submission Guidelines
Maximum authorship. In the research track, the number of submissions allowed per author is limited to 7 (seven) maximum per cycle.
Anonymity. The review process for the research track will be double-blind. The submitted document should omit any author names, affiliations, or other identifying information.
Formatting Requirements. Submissions must be in English, in double-column format, and must adhere to the ACM template and format (also available in Overleaf); Word users may use the Word Interim Template. The recommended setting for LaTeX is:
\documentclass[sigconf,anonymous,review]{acmart}
Submissions must be a single PDF file: 8 (eight) content pages as main paper, followed by references and an optional Appendix that has no page limits. The Appendix can contain details on reproducibility, proofs, pseudo-code, etc. The first 8 pages should be self-contained, since reviewers are not required to read past that.