Li, Jiaming
Lang, Lang
Zhu, Zhenlong
Wang, Haozhao
Li, Ruixuan
Xu, Wenchao
Chapter History
First Online: 17 September 2023
Ethical Statement
: This research work on feature interaction networks and click-through rate (CTR) prediction was conducted with a focus on developing a novel framework for improving the accuracy of CTR prediction models. The research work was conducted with adherence to ethical principles and standards of research integrity. The research does not involve any human subjects or any sensitive data, and all the data are evaluated from the most mainstream public datasets in CTR prediction task, so no ethical approval is required. The research work was conducted with the aim of advancing the state-of-the-art in CTR prediction models, and the results of this study can have potential implications for businesses and industries that rely on CTR prediction models. The authors acknowledge the contributions of prior research in this area and have given appropriate credit to previous works. The authors have also disclosed any potential conflicts of interest related to this research work. The research work was conducted with transparency and openness, and has been peer-reviewed and vetted.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.