A Synthetic User Behavior Dataset Design for Data-Driven AI-Based Personalized Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
It is envisioned that wireless networks of the future will support personalized, fine-grained services and decisions by predicting user satisfaction in real-time using machine learning and big data analytics. Data-driven personalization will empower wireless networks to further optimize resources while maintaining user expectations of networks. In order to design, test, and validate research ideas related to wireless network personalization, acquiring data is essential. However, datasets that comprise user behavior and corresponding user satisfaction information are generally not published due to privacy and confidentiality concerns. To account for this, in this paper, we propose a synthetic dataset design methodology to generate labeled user behavior data with ground truth satisfaction values which mimic the real characteristics of real datasets. Finally, we conduct sample user satisfaction prediction experiments using several machine learning algorithms.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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