- Paper Submission: February 2, 2023
- Author/Reviewer Interaction and Reviewer Discussion: April 6-27, 2023
- Final Notification: May 16, 2023
- Camera-Ready: June 4, 2023
- Conference: August 6-10, 2023
All deadlines are at 11:59 PM Anywhere on Earth (AoE).
Access the submission site at OpenReview.
KDD is a dual track conference hosting both a Research and an Applied Data Science (ADS) track. A paper should either be submitted to the Research or the ADS track but not both.
Aim and Scope
The KDD ADS track is broadened this year to include the following:
- Deployed systems: Description of a system that solves a significant real-world problem and their challenges with privacy, security, fairness, robustness, or troubleshooting (e.g., fraud detection in financial institutions using data science).
- Multiple domains: Methods tailored for a specific applied domain, but whose insight bridges multiple domains; insights into the use of data mining tools that span multiple domains (e.g., a method for analysis of tweets that is useful for other short texts).
- Novel combinations: Using existing data mining tools but combining them in novel ways (e.g., a method to analyze music, which combines many data mining tools).
- Data sets: Applied scientific work on handling large or complex data sets from specific domains (e.g., noisy medical records).
- Ethics: Papers that study the ethics of AI for specific types of domains.
- Others: Work that would not fit into the Research track but will appeal to applied data science researchers and practitioners.
The paper must be aimed at a broad audience of applied data scientists. Topics of interest include, but are not limited to the following:
- Recommendation systems
- Personalization and contextualization
- Search & Information retrieval
- Conversational AI/Dialog systems/Intelligent assistants
- Machine translation & Multilinguality
- Question answering and NLP applications
- Knowledge collection, mining, and management
- Multi-modal knowledge discovery and data mining
- Social networks
- Human-computer interfaces
- Scalability, Parallel & Distributed systems
- Privacy, Fairness, Accountability, Transparency, Ethics, and Explainability
- Anomaly detection, Adversarial attacks & Robustness
- Domain-specific applications (e.g., Health, Legal, etc.)
Submission and Formatting Instructions
Submissions to the ADS track are single-blind—author names and affiliations should be listed.
ADS track submissions are limited to 9 pages (excluding references), must be in PDF, and use ACM Conference Proceeding template (two column format). The recommended setting for Latex documents is:
Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit. As in previous years, the supplementary material should be included in the same pdf file with the main manuscript. The main body of the paper should be self-contained, since reviewers are not required to read the supplementary material. The supplementary material will not be included in the proceedings.
Submissions violating these formatting requirements will be desk-rejected.
The Word template guideline can be found here: https://www.acm.org/publications/proceedings-template
The Latex/overleaf template guideline can be found here: https://www.overleaf.com/latex/templates/association-for-computing-machinery-acm-sig-proceedings-template/bmvfhcdnxfty
All authors will be required to register as reviewers for KDD. Not all authors will be requested to provide reviews, but if an author is requested to provide up to three timely and good quality reviews for KDD and declines to do so when requested, their submission will be rejected.
Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication in an archival venue. KDD submissions must not be in concurrent submission to any archival conference or journal during the KDD review period. Papers can be submitted to arXiv with the same title and abstract during the review process.
Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available during the review process, unless there is an inevitable reason that they cannot be released (e.g., it requires data from a specific company or it is medical data where there is no public alternative). Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility; this includes model parameters, experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission. In the case where data cannot be released, authors are encouraged to include experiments on relevant public datasets and/or create simulated data with the same properties.
Every person named as the author of a paper must have contributed substantially to the work described in the paper and/or to the writing of the paper. Every listed author must take responsibility for the entire content of a paper. Persons who do not meet these requirements may be acknowledged, but should not be listed as authors. Authorship, including ordering, cannot be modified after the submission deadline.
Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library. The rights retained by authors who transfer copyright to ACM can be found here.
Official Publication Date
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date for KDD 2023 is on or after July 15, 2023. The official publication date affects the deadline for any patent filings related to published work.
By submitting paper(s) to KDD 2023, the authors agree that the reviews and discussions may be made public for all accepted papers.
At least one author of each accepted paper must register for KDD to present their work in person.
Ravi Kumar (Google, Mountain View, CA)
Fatma Ozcan (Google, Sunnyvale, CA)
Jieping Ye (Alibaba DAMO Academy, Hangzhou, P. R. China)
ADS Track PC co-Chairs of KDD-2023