Matching Mobile Applications for Cross-Promotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the mobile app market grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. Similar to the case of recommender systems, the challenge is how apps can be recommended to the right users and how consumers can find the right apps. This paper studies a new mobile app ad framework, cross-promotion (CP), which is to promote new “target” apps within other “source” apps. With unique random matching experiment data, we empirically test the important determinants of ad effectiveness. We then propose a machine-learning-based framework to optimally match source apps to target apps to improve ad effectiveness in terms of app downloads and postdownload usages. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of CP campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. The paper has important managerial implications because it provides direct guidance to better utilize CP for app developers and to leverage data analytics and machine-learning models for platform managers. It also provides policy implications on the trade-off between utility and privacy in the growing data economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it