Research on predicting the adaptability of employment policy for senior college students based on collaborative filtering algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Based on the wide application of collaborative filtering algorithm in the current field of graduate employment, this paper introduces it into the employment recommendation mechanism of senior college students and takes it as one of the auxiliary means to formulate the employment policy for senior college students.By studying the implementation effect of employment policy, so as to explore the adaptability of employment policy.Through the time series prediction method based on neural network, the prediction model of employment policy adaptability of higher vocational tertiary students is constructed.Compare the prediction performance of this paper's prediction model with other models, predict the employment policy implementation effect through this paper's model, and finally, construct an evaluation system of employment policy prediction results to evaluate the model prediction results.The prediction fit of the model of this paper is 0.8644, and the average relative prediction error is 0.35%, which is the best performance among all prediction models.In the prediction of the employment of higher vocational college students in province A, the number of employment of higher vocational college graduates is positively correlated with the average annual income level and the market share of graduates, and negatively correlated with the total number of gaps between faculty and students in the institutions and the amount of education expenditure.The overall score of the employment policy implementation effect predicted by the prediction model in this paper is 88.8, which is a good evaluation result.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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