Skill UI

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

A comprehensive guide for authors submitting papers related to web search, ranking, and data mining to WSDM. It provides critical advice on conference fit assessment, proper manuscript re-framing, required evidence (retrieval/ranking baselines, platform-aware evaluation), and how to position the work against competing top venues like SIGIR and KDD.

A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.

This guide helps researchers assess if their work correctly spans the tri-community area of Information Retrieval, Data Mining, and Knowledge Management. It provides a detailed framework for comparing CIKM against major academic venues like SIGIR, KDD, WSDM, and SIGMOD/VLDB, ensuring the paper's scope is optimally matched to the target conference.

This skill provides a strategic guide for authors evaluating if their AI/data mining manuscript is a suitable fit for the ECML PKDD conference. It covers scope definition, evidence requirements, academic framing, and strategic positioning relative to major global venues (e.g., ICML, NeurIPS). Use this to refine your submission narrative and maximize your chances of acceptance.

This guide assists researchers in determining the best fit for their data mining research project. It compares the contribution against major conferences (ICDM, KDD, SDM, etc.) and helps decide the appropriate track (Research, Applied, Blue Sky) based on the nature of the mechanism, evidence, and intended scope for optimal academic impact.

A comprehensive guide detailing how to structure, package, and validate research artifacts for major machine learning conferences like KDD. It covers best practices for creating anonymous, reproducible repositories, handling scale claims, and providing robust evidence for different research tracks (Research, ADS, Datasets).

A comprehensive guide detailing the strategy and structure for author responses to peer reviews (rebuttals) in top-tier academic conferences like KDD. It covers the unique constraints, such as the prohibition of hyperlinks, how to anchor claims using specific coordinates (e.g., "Table 3, column 4"), and structuring responses to address multiple reviewer concerns while targeting the Area Chair's decision.

A comprehensive guide for authors preparing their accepted KDD paper for ACM proceedings. It covers the entire production pipeline, including page budgeting (9 content pages, 3 pages for references/appendix), managing e-rights and TAPS uploads, de-anonymization steps, and ensuring proper Digital Library metadata for final publication.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

This guide provides a sophisticated framework for structuring the related work section when submitting research to KDD. It instructs authors on how to position their work within the deep academic lineage of data mining, comparing their novelty not just to prior KDD volumes, but also to major ML flagships (ICML, NeurIPS) and specialized venues (WWW, ICDM). Learn to articulate mechanism-level differences and avoid common citation traps.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.