Scalable Machine Learning Algorithms for a Twitter Followee Recommender System
Why this work is in the frame
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Bibliographic record
Abstract
Recently, machine learning (ML) algorithms have been employed in social networking recommender systems. In this paper, a Twitter recommender system is simulated by a multi-agent system that can be used to provide the users with a list of useful recommendations, specifically a list of users (i.e., followees) that a user is interested in following. The simulator is used to test the scalability of a machine learning algorithm (i.e., Neural Network, Multilayer Perceptron) for data analysis with parallel implementation on multi-node distributed systems. The distributed environment is simulated by a multi-agent modeling. The initial parameters that should be set up on the simulator include the number of nodes, the algorithm employed in the simulated recommender system, and the actual followees and followers information. The experimental results were obtained on three distinct datasets for evaluating the accuracy and the execution time of a simulated recommender system when testing the ML algorithm in different scenarios.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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