Customer Product Choice Recommendation By Association Rules And Learning Models
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
Online stores and apps attracts customer at various levels. So approaches need to be more effective by analyzing the behavior of visiting customer. Many of researcher has proposed different models of customer product recommendation system. This paper introduces a novel Customer Product Recommendation by Rules and Ensemble Model (CPRRESM) framework designed to enhance purchase prediction accuracy for small-scale retail stores with limited data resources. The proposed approach integrates Apriori-based association rule mining for pattern discovery, Z-score normalization for feature standardization, and a Gradient Boosting ensemble model for efficient learning. By combining rule-based insights with ensemble learning, CPRRESM effectively captures customer purchasing behavior and dynamic preferences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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