Evaluation on a Personalized Mobile Advertising System: a Comparative Approach
Bibliographic record
Abstract
Along with the high proliferation of mobile phones and other mobile devices, research on the use of short messaging service&#;SMS&#; to access customers through their handheld devices has gained much attention, which is termed as mobile advertising. In order to make the best use of mobile advertising to benefit companies and customers becomes more emergent. One of the most important and successful factor that will bring more positive attitudes towards mobile advertising and induce customers to behave positively is personalization, which has been confirmed in many prior studies. Therefore, it’s necessary and essential for researchers to design an effective system capable of recommending personalized mobile advertising to mobile users. The purpose of this paper is to fulfill this task. We present such a kind of personalized mobile advertising system based on Bayesian Network. Then, we brought out a thorough evaluation of our system in a laboratory environment. Experimental results showed better performance of our system in furnishing personalized mobile advertising than conventional method (random advertising).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".