A Novel Classifier for a Kansei Recommender System
Why this work is in the frame
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Bibliographic record
Abstract
We propose a novel classifier for a Recommender System which is based on a Kansei Model in this paper. We called this Recommender System as Kansei Recommender System (hereafter, we denoted as KRS algorithm). The purpose of building KRS algorithm is to reduce the time of training data from database and give more precise recommender items for consumers by considering their Kansei (a Japanese word which means the consumers' psychological feeling). To build a novel classifier, we divide the KRS algorithm into two parts of algorithms: (1) Algorithm 1 is proposed to extract Kansei factors (score 1) and evaluation factors (score 2) from consumers' shopping items. (2) Algorithm 2 is proposed to give a training dataset that is to fit the scored value of Kansei model. Combining two algorithms, we get a novel classifier for a KRS algorithm. We give an architecture of KRS algorithm based on the database of on-line shopping market in the end of this paper.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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