Generating Artificial Data for Scalability Test of a Followee Twitter Recommender System
Bibliographic record
Abstract
The current methods for the evaluation of the scalability of recommender systems measure the scalability of the whole application after deployment on a cloud by calculating the running times of the application when increasing the number of nodes. This method requires the complete development and implementation of a whole application. To be able to test the recommender system during the development phase, the major problem to test the scalability and accuracy is collecting real data (i.e. social data), which is a time-consuming task and sometimes it is not possible due to privacy concerns. This thesis proposes measuring the scalability of Twitter recommender systems by simulating the software, which processes a large number of artificial tweets. A method is introduced and validated for producing artificial tweets to test a recommender system. This method of producing artificial tweets is based on using analytical modeling, tf-idf and bag-of-words model. A simulator is developed to test the scalability of a recommender system and underlying distributed environment.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.013 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".