KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Association for Computing Machinery (ACM), 2024.

Under the general editorship: R. Baeza-Yates, F. Bonchi

It is our pleasure to welcome you to the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining---KDD'2024 in Barcelona, Catalonia. This year's conference continues its tradition of being the premier forum for presentation of research results and experience reports on knowledge discovery, data science, and machine learning. The mission of the conference is to provide the best venue for advancement, education, and adoption of the "science" of knowledge discovery and machine learning from all types of data; to share novel methodologies that fulfill the needs of heterogeneous applications and environments and identify new directions for future research and development. These ideas have the potential to shape and impact our society and environment, becoming particularly important with the emergence of AI in all fields. So, KDD provides researchers and practitioners a unique opportunity to share their perspectives with others interested in various aspects of data science and machine learning.

KDD'24 has a program of three keynotes (Sanjeev Arora, Tanya Berger-Wolf and Xihong Lin), one panel on generative AI, 411 research track papers, 151 applied data science (ADS) track papers and eight invited talks, 30 workshops, 34 tutorials (nine of them hands-on), nine special days (one online for India), and three KDD cups. We have introduced two new special days, one in Responsible AI and another in European Data Science given the location of the conference. We also added one extra poster session to have more time for posters presentations. For second time we used Openreview for the research and ADS tracks with the goal to further improve the review quality and facilitate the interaction between reviewers and authors. We hope that you will find this program interesting and thought-provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.

Chapters

From Variability to Stability: Advancing RecSys Benchmarking Practices

Shevchenko V., Belousov N., Vasilev A. et al., , in: KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.: Association for Computing Machinery (ACM), 2024. P. 5701–5712.

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel ...

Added: November 24, 2024

Keywords: Data mining