The Conference on Information and Knowledge Management (CIKM) is a premier forum for researchers and practitioners from academia, industry, and government bodies to share technologies and state-of-the-art research on the emerging aspects of artificial intelligence, data mining, data management, and information retrieval. The resource track at CIKM 2025 provides a unique opportunity to researchers to share and highlight their latest technologies that enable intelligent decision-making, predictive analytics, and machine learning.

All deadlines are at 11:59pm in the Anywhere on Earth (AoE) time zone.

We welcome submissions on all topics in the general areas of artificial intelligence, machine learning, data science, databases, information retrieval, and knowledge management. An ideal resource paper’s topics of interest include, but are not limited to, the following areas:

Recourse papers must be no more than 4 pages long including appendices, plus unlimited pages for references. Supplementary material: It is allowed to cite supplementary materials including source code, videos, datasets, and demonstration prototypes that are accessible via online platforms like GitHub. Reviewers have the discretion to decide whether or not they will review such materials. The review of the resource papers will be single-blind, which means that the authors should include their names and affiliations in the paper. All submissions will be reviewed by the Program Committee of the Resource track, who will evaluate the novelty of the technical features and/or research being presented, the research and/or development challenges, its expected impact, and its timeliness and relevance for the CIKM audience of practitioners and researchers. Manuscripts should be submitted to CIKM 2025’s Easychair page in PDF format using the ACM’s two-column template “sigconf”, see https://www.acm.org/publications/proceedings-template. At least one author of each accepted paper must register to present the work on-site in Seoul, Korea as scheduled in the conference program. All papers should be submitted via Easychair:
https://easychair.org/conferences/?conf=cikm25 Papers that include text generated from a large-scale language model (LLM), such as ChatGPT, are prohibited unless this produced text is presented as a part of the paper’s experimental analysis. AI tools may be used to edit and polish authors’ work, such as using LLMs for light editing of their text (e.g., automate grammar checks, word autocorrect, and other editing of author-written text), but text “produced entirely” by generative/AI models is not allowed.

Papers presenting a dataset or a benchmark must publish the datasets and metadata using a dataset-sharing service (e.g., Zenodo, Datorium, Dataverse, or any other dataset-sharing service that indexes your dataset and metadata and increase the re-findability of the data) that provides a DOI for the dataset, which should be included in the dataset paper submission. Ethical considerations must be discussed. Authors are encouraged to include a description of how they intend to make their datasets FAIR [1]. We would also encourage authors to consider addressing the questions covered in the Datasheets for Datasets recommendations [2]. For papers detailing code resources, such as libraries, external tools, frameworks, etc., it is imperative that authors adhere to rigorous standards in code sharing and ethical considerations. Specifically, authors should ensure that their code resources are made publicly available through reputable, code-sharing platforms such as GitHub, GitLab, Bitbucket, or similar services that facilitate code access and enhance code reusability, thereby ensuring transparency and reproducibility. We advocate for the incorporation of best practices in code documentation and versioning, urging authors to provide comprehensive documentation covering code functionalities, dependencies, and potential limitations, fostering transparency and usability in research practices. The reviewing guidelines for the resource paper track will focus on the following criteria: Novelty: Availability: Utility: Predicted Impact:

Resources are expected to be available as described, where “available” means that most researchers in our community could obtain and make use of the resource without strongly limiting the research they can perform with it. Datasets are expected to be collected in accordance with institutional review board standards and ACM standards of ethics. Reviewers are instructed not to use their reviews as an advocacy platform for these issues but to do what they can to help authors bring their resources to fruition.

Disclosure of funding and competing interests: Authors are required to provide an explicit disclosure of funding (financial activities supporting the submitted work) and competing interests (related financial activities outside the submitted work) that could result in conflicts of interest in a section (e.g., “Acknowledgments”) that should be added in the submitted version for review.

It is not allowed to submit papers that are identical (or substantially similar) to versions that have been previously published or accepted for publication or that have been submitted in parallel to other conferences (or any venue with published proceedings). Such submissions violate our dual-submission policy. However, submissions are permitted for papers presented or to be presented at conferences or workshops without proceedings or with only abstracts published. Authors may also submit work already available as a preprint (e.g., in arXiv).

Before paper submission, authors should carefully review ACM’s authorship policy, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects

All authors must adhere to the ACM Conflict of Interest policy. For full details, please visit this site:
https://www.acm.org/publications/policies/conflict-of-interest

Submissions that fail to adhere to the length, or formatting requirements, or violate ACM’s policies on academic dishonesty—such as plagiarism, author misrepresentation, or falsification—may be subject to desk rejection by the chairs.

All authors and participants must adhere to the ACM’s discrimination policy. For full details, please visit this site:
https://www.acm.org/special-interest-groups/volunteer-resources/officers-manual/policy-against-discrimination-and-harassment

For more information, contact the resource chairs: cikm2025-demo@easychair.org
[1] Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), pp.1-9. [2] Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J.W., Wallach, H., Iii, H.D. and Crawford, K., 2021. Datasheets for datasets. Communications of the ACM, 64(12), pp.86-92.