[DL] CIKM 2021 - Call for Full Papers
aldo.lipani at acm.org
Sat Apr 10 23:33:48 CEST 2021
Apologies for cross-posting.
CIKM 2021: The 30th ACM International Conference
on Information and Knowledge Management
1 - 5 November 2021, Online
The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on artificial intelligence, search and discovery, data mining and database systems, all at a single conference. CIKM is uniquely situated to highlight technologies and insights that materialize the big data and artificial intelligence vision of the future. CIKM 2021 will take place online in a lively and interactive manner.
AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)
Full Paper Abstract Submission:
May 19, 2021 (anywhere in the world)
Full Paper Submission Deadline:
May 26, 2021 (anywhere in the world)
August 9, 2021 (anywhere in the world)
Final Version Submission:
Aug 23, 2021 (anywhere in the world)
Topics of Interest
We encourage submissions of high quality research papers on all topics in the general areas of artificial intelligence, data science, databases , information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas:
* Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)
* Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information
* Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)
* Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
* Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability)
* Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
* 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)
* 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)
* Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)
* Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)
* Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
* Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)
* Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media)
Authors are invited to submit original, unpublished full-length research papers that are not previously published, accepted to be published, or being considered for publication in any other forum. Full-length papers should satisfy the standard requirements of top-tier international research conferences.
Manuscripts should be submitted to CIKM’21’s Easychair site in PDF format, using the ACM sigconf template, see https://www.acm.org/publications/proceedings-template. Full papers cannot exceed 9 pages plus unlimited references. Rejected full papers will not be considered for publication as short papers. Paper review will be double-blind, and submissions not properly anonymized will be desk-rejected without review. At least one author of each accepted paper must register to present the work as scheduled in the conference program, and may include both oral presentation and poster sessions. Additional details for running the online conference will be published on the website shown above.
Dual Submission Policy
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. Such submissions violate our dual submission policy. There are several exceptions to this rule:
* Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
* Submission is permitted for papers that have previously been made available as a technical report (or similar, e.g., in arXiv). In this case, the authors should not cite the report, so as to preserve anonymity.
ACM Policy Against Discrimination
All authors and participants must adhere the the ACM discrimination policy.
For full details, please visit this site:
PC Chair Contact Information
For more information, contact the appropriate PC chairs:
Full Paper Track Email: cikm2021-full at easychair.org
J. Shane Culpepper, RMIT University, Australia
Helen Huang, The University of Queensland, Australia
Hanghang Tong, University of Illinois at Urbana-Champaign, USA
Dr. Aldo Lipani | aldolipani.com
Asst. Prof. in Machine Learning
University College London (UCL)
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