An Emotion-Aware Recipe Generation Framework Using Distilbert and Large Language Models
Bibliographic record
Abstract
Traditional food recommendation systems often provide static meal suggestions based on previous user behavior, without the ability to develop tailored recipes based on current emotional states. This study presents an emotion-aware recipe generating framework that dynamically generates personalized culinary recommendations based on user-provided emotional inputs. The system uses a pre-trained DistilBERT model for sentiment classification and keyword-based food concept mapping to match emotions to relevant food categories. These inputs are subsequently processed by Falcon-7B-Instruct, a big language model, which uses rapid engineering to generate entire, structured recipes in plain language that include both ingredients and preparation instructions. Unlike existing methods that rely on curated food databases, our approach makes use of the generative capabilities of large language models (LLMs) to provide context-sensitive, emotionally meaningful recipe recommendations. The overarching goal is to improve user engagement and fulfilment by tailoring nutritional recommendations to individual emotional states, resulting in a novel junction of affective computing and customized nutrition.
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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 | 0.000 | 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 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".