* We apologize if you receive multiple copies of this CfP * * For the online version of this Call, visit: https://cikm2024.org/call-for-industry-day-papers/
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CIKM 2024: 33rd ACM International Conference on Information and Knowledge Management
Boise, Idaho, USA
October 21–25, 2024
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The Conference on Information and Knowledge Management (CIKM) provides an international forum for the presentation and discussion of research on information and knowledge management, as well as recent advances in data and knowledge bases. The purpose of the conference is to identify challenging problems facing the development of future knowledge and information systems, and to shape future directions of research by soliciting and reviewing high-quality, applied and theoretical research findings.
We call for technical talks which will cover how topics of interest relevant to the broader CIKM community, including but not limited to knowledge management, information retrieval, efficient data processing, neural and large language models, evaluation, recommender systems, data mining, and others found in the CIKM ‘24 Call for Papers are used in an industrial setting. Possible topics include how machine learning is put to use in practical scenarios, how user behavior can be observed and interpreted, how to improve systems in practice, how industrial pipelines can be optimized, and how scale is a challenge in more ways than the obvious. We also encourage talk proposals from small companies, such as startups or spin-offs from either a university project or a large company.
-------------------------- Key Dates --------------------------
* Submissions Due: June 20th, 2024 * Notifications: July 16, 2024 * Camera ready for abstracts: August 8, 2024
(All deadlines are at 11:59 pm AOE)
The Industry Day of CIKM ’24 will be held on Monday 21st Oct 2024 in Boise, Idaho, USA.
-------------------------- Topics of Interest --------------------------
Talks may address challenges, solutions, and case studies of interesting and innovative systems in areas including but not limited to: * Innovative approaches used in deployed systems and products * System design from industry practitioners which identify best practices and design principles for machine learning systems and their scalability aspects * Metrics and measurement techniques used to understand performance of production systems * Practical challenges such as data, privacy, integrity, scale, regulation, etc. * Domain specific challenges and niche focuses * Connections with academia to solve interesting problems, including talk proposals from academics spending time in industry, or vice-versa, covering insights for other practitioners
We encourage talk proposals from small companies, such as startups or spin-offs from either a university project or a large company.
-------------------------- Paper Submissions --------------------------
Proposals should be at most 2 pages and follow the ACM format. Formatting guidelines are available at the ACM Website (use the ˮsigconf” proceedings template). https://www.acm.org/publications/proceedings-template
Submissions should include: * Title and abstract * Speaker's bio * Relevance to above themes and CIKM topics * CIKM is a technical conference, so preference will be given to talks describing applied research and technical challenges rather than product presentations. * Speakers will be asked to confirm their presence at the conference if their submission is accepted.
Submissions are not anonymous and should contain speaker details. Proposals should be submitted electronically via EasyChair: https://easychair.org/conferences/?conf=cikm2024
The authors of accepted proposals will be invited to submit an abstract to be published in the conference proceedings. Each presentation will be 15-20 minutes long including Q&A.
-------------------------- Chairs Contact Information --------------------------
For more information, contact the Industry Day chairs: cikm2024-industry [at] easychair [dot] org
Ilaria Bordino, UniCredit, Italy Udayan Khurana, IBM Research, USA Marc Najork, Google DeepMind, USA