Wang, Yizhuo
Jiang, Renhe
Liu, Hangchen
Yin, Du
Song, Xuan
Chapter History
First Online: 17 September 2023
Ethical Statement
: In this study, we introduce an innovative technique for predicting the next app by leveraging user mobile app behavior data. To implement this, our work utilizes two datasets - China Telecom app usage dataset, which is publicly available, and a distinct proprietary dataset acquired through collaboration with Huawei. We have strictly followed ethical guidelines to protect the privacy and integrity of individuals and entities involved in this study.
: The China Telecom app usage dataset has been widely used in previous research and is considered ethically acceptable. Meanwhile, the Huawei app usage dataset is provided by our collaborative partner, Huawei. It is important to mention that the visualizations in our case study section do not raise any ethical concerns. This is because the users, locations, and apps of both datasets have been anonymized to protect user privacy.
: Our study follows ethical principles to handle sensitive data responsibly. We obtained permission for datasets, ensured anonymity and privacy, and complied with data protection regulations. We did not disclose any data to unauthorized parties and put in place security measures to prevent misuse or unauthorized access.In summary, our research methodology prioritizes ethical considerations, utilizing anonymized data and safeguards to protect sensitive information. Our commitment affirms ethical guidelines adherence with reliable results, ultimately contributing to progress in predicting mobile app user behavior based on usage data, while ensuring the accuracy and dependability of our findings.
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.