User Persona in Personalized Wireless Networks: A Big Data-Driven Prediction Framework
Bibliographic record
Abstract
Wireless network personalization is an emerging technology that has considerable potential to achieve the ultimate balance between resource allocation and user satisfaction. One of the main enablers of personalized networks is the continuous monitoring and prediction of dynamic user satisfaction levels in various contexts. Accurate satisfaction prediction requires a lot of data, and unfortunately, data and the process of acquiring it are expensive. A closer look at user behavior and satisfaction levels reveal that certain users share certain behavioral similarities. A group of users who share similar user behavior and satisfaction patterns is referred to as a persona. Associating users with preexisting user personas will enable networks to provide highly personalized services with a minimal amount of data, thereby improving the efficiency of personalized networks. In this paper, we propose a novel big data-driven framework to predict user personas in personalized wireless networks. Also, we conduct a comprehensive study to investigate the impact of different amounts of data and confidence levels on the performance of the overall framework. Finally, using a simulated personalized wireless network, we compare the behavior of different personas in terms of the amount of saved resources and achieved satisfaction levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".