Model and algorithm for supporting decision on selection of products for recommendation to user based on analysis of statistical implication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article considers the issues of analyzing data that the consumer encounters when choosing products and services. The problem is extracting useful information that allows offering the user new products and services depending on his preferences. This problem is localized by recommender systems focused on using the data mining methods, such as classification, clustering, analysis of association rules - a machine learning method that detects relationships between variables in databases. Compared to other methods, the advantage of the association rule-based recommender method is its transparency: the method can show the user the inference mechanism used to make decisions. Association rule-based recommender systems use two measures that are widely popular and evaluate sets of elements and create sets of association rules, the support measure and the confidence measure. However, to get better recommendations, the quality of association rules and the way sentence ranking should be measured by some objective measure. There has been developed a model and a decision support algorithm for the choice of products for recommendations to the user based on the statistical implication analysis method. In the proposed solutions, support and credibility measures are used to create association rules; a measure of the intensity of the statistical implication is used to filter the set of rules and to rank the recommendations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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