The DOI matches its Crossref record.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Abstract
We welcome you to the 2023 ACM International Conference on Information and Knowledge Management (CIKM 2023) in Birmingham, UK! Birmingham is located in the West Midlands of England, an area that was instrumental in driving the Industrial Revolution, and an excellent place for researchers to gather and discuss the opportunities and challenges of the next, AI- and data-driven, revolution. CIKM is traditionally held once a year at different locations all over the world. Although deemed to be a full physical conference, CIKM 2023 live streams selected sessions over an Internet-based virtual platform, which enables remote delegates to attend the conference. We are also planning to apply some novel ways for the online delivery of talks, with the aim of accommodating different time zones. CIKM 2023 is the 32nd running of the conference and remains the only conference that focuses on the management of both structured and unstructured data, accepting research papers covering databases, information retrieval, and knowledge management sub-disciplines of Computer Science. CIKM again includes an Industry Day as a full-day event with 14 accepted talks. Papers are solicited in five tracks: full research papers, short research papers, resource papers, applied research papers and demonstration papers. 16 workshops will enable researchers to discuss the latest breaking results and 9 tutorials will provide interested delegates with useful basic insights into trending fields. A PhD Symposium will bring together senior researchers with PhD students to discuss the exciting work of these early-career researchers who will contribute to shaping tomorrowﾒs world.
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Cited by
How Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3678004 · Cited by 242
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences
Shankar, S., Zamfirescu-Pereira, J. D., Hartmann, B., Parameswaran, A., & Arawjo, I. (2024) · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676450 · Cited by 151
Wang, F., Zhang, Z., Zhang, X., Wu, Z., Mo, T., Lu, Q., Wang, W., Li, R., Xu, J., Tang, X., et al. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3768165 · Cited by 106
AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.
Ghosh, M., Mukherjee, S., Ganguly, A., Basuchowdhuri, P., Naskar, S. K., & Ganguly, D. (2024) · Methods · doi:10.1016/j.ymeth.2024.04.005 · Cited by 27
SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines
Shankar, S., Li, H., Asawa, P., Hulsebos, M., Lin, Y., Zamfirescu-Pereira, J. D., Chase, H., Fu-Hinthorn, W., Parameswaran, A. G., & Wu, E. (2024) · arXiv · doi:10.48550/arxiv.2401.03038 · Cited by 27
Metadata
- Publisher
- ACM
- Published
- 2023-10-21
- OpenAlex
- W4387841511
- Cited by
- 433
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
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