The 35th ACM International Conference on Information and Knowledge Management (CIKM) offers a forum for academia and industry to present cutting-edge research on artificial intelligence, foundation models, search and discovery, text and data mining, and database systems.

The Applied Research Track invites submissions from both academia and industry that focus on advancing the understanding of issues related to deploying foundation models, IR, NLP and AI at scale. Unlike the Research Track, the Applied Research Track concentrates on applied work, such as describing the implementation of a system, data acquisition, or application of a methodology that addresses a significant real-world problem and demonstrates measurable benefits and impact. We invite authors to submit papers that showcase their research work’s real-world impact and demonstrate practicality and scalability. The emphasis is on papers that either solve or advance the understanding of issues related to deploying data science and AI technologies in real-world settings.

Submissions should clearly outline how the work has been deployed or released and for how long, or how the work is planned to be deployed or released, and what its potential impact is in the real world.

Scope

We invite submissions with their main focus on applied and deployed work, on the same topics of interest as the CIKM 2026 Research Track. The submissions must be substantiated by a system launch, data release, or other practical application evidence. Submissions must include a set of metrics related to the post-launch performance and usage of the described system or solution. Submissions that do not provide this quantification will be desk-rejected without review. For example, the following types of submissions that do not meet the criteria for real-world deployment will be desk-rejected:

• Applications that are tested only with synthetic data;
• Applications that are not Open Source or that do not provide all the necessary implementation details to reproduce the results exposed in the submitted paper;

Papers should present the problem, its significance to the application domain, the decisions and tradeoffs made when making design choices for the solution, how any challenges in areas such as data collection, modeling, and deployment in constrained environments were overcome, and the lessons learned from successes and failures. It is perfectly fine if the underlying machine learning approaches are not fundamentally groundbreaking. However, it must be clear how these machine learning approaches are applied to the problem domain and deployed in a real-world system. For systems not yet deployed, submissions must provide strong evidence of real-world applicability, including realistic evaluation settings and a clear path to deployment.

Papers should be aimed at a broad audience of applied data, IR, NLP, AI scientists.