35th ACM International Conference on Information and Knowledge Management (CIKM 2026)

The 35th ACM International Conference on Information and Knowledge Management (CIKM 2026) is dedicated to research on information and knowledge management, as well as recent advances on data and knowledge bases.

Key Topics

• Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information credibility)
• Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)
• Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
• Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)
• Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
• Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability)
• Users and interfaces for information and data systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)
• Information access and retrieval (e.g., ad hoc and web search, facets and entities, question answering and dialogue systems, retrieval models, query processing, personalization, recommender and filtering systems)
• Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)
• Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)
• Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)
• Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
• Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media)