Editorial: Scalable recommender systems with formal guarantees
Birgit Holmberg1*
1School of Public Health, Federal University of Rio Verde, Brazil
* Correspondence: birgit.holmberg@rioverde.example
Trans. Intell. Comput. Syst. 2024, 2(1), 62-72; https://doi.org/10.58304/q8yafb74
Introduction
Over the past decade, work on editorial: scalable recommender systems with formal guarantees has grown from a specialist concern into a field with its own methods, journals and communities. This editorial introduces the contributions in this issue and places them in a wider context.
Three themes run through the articles. First, the authors pay close attention to the quality of the underlying evidence and to the assumptions that connect it to the conclusions. Second, they take reproducibility seriously: data, code and protocols are shared wherever possible. Third, they are candid about the limits of their findings.
In this issue
We are grateful to the reviewers, who gave generously of their time, and to the members of the editorial board for their advice. We also thank the editorial office for its careful work on every manuscript.
The journal will continue to publish work that is rigorous, clearly written and openly available. We welcome suggestions from readers and invite submissions in all areas within our scope.
Declarations
Funding
This work was supported by the National Science Foundation under grant number 2301234 and by institutional research funds.
Competing Interests
One author is a consultant for a company that manufactures related equipment. The company had no role in the design of the study or the interpretation of the results.
Declaration of Generative AI Use
The authors used the tool to improve the language of the Introduction and the Discussion. All content was checked and edited by the authors, who take full responsibility for it.
Copyright and License
© 2024 Aurora Computing Society