Empirical Study on Personality Trait Classification by Food Related Preferences
Why this work is in the frame
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Bibliographic record
Abstract
Human personality is a combination of the behavior, emotion, motivation and thinking pattern and has great impact on a person's life, health, and other related preferences. Food preferences provide rich information for studying personality of a person. In this paper, we conduct an empirical study to predict human personality based on restaurant review on food and other related preferences. We choose the category to `judge/perceive' from the 4 categories of 16 personality traits. A data set is built from a survey of 100 people based on a questionnaire about their food related behavior along with standard personality traits. A classification algorithm is proposed to classify the participant's personality from his/her food preference and surrounding environment on a restaurant, using reinforcement learning that utilizes temporal difference, model based, and on policy techniques. We compare our proposed classification results with standard classification solutions for personality detection to determine the performance accuracy of our proposed model.
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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) | 0.008 | 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