Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems
Bibliographic record
Abstract
The aim of this research is to show how social networks can be used for marketing purposes. This is implemented with the assistance of learning algorithms. The method proposed in this research is based on the analysis of “Support Vector Machines”, which facilitates analysis of all information gathered from the social websites. It differs from other methods currently being used by social networking websites, which do not take advantage of classification. By using public information from social networks, a dataset was formed. It comprised of a thousand users and seven features. The examined features were location, age, gender, occupation, relationship status, and average travel time/year. In this research, the dataset will be examined twice: first using a regular SVM; and next by using “Weighted Feature Support Vector Machines”. For the latter, to assign weights, a method called “Pairwise Comparison” will be used to rank the importance of features.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".