1. Automated Recipe Completion using Multi-Label Neural Networks
Alexander Politowicz
12/12/2018

2. Executive Summary
Using partial list of ingredients, predict missing ingredients
Completed! Model attempts prediction of missing ingredients
Unfortunately, accuracy not great; room for improvement
- Sesame Oil
- Sichuan peppercorns
Cherry tomatoes
Paprika

3. Approaches and Data Analysis
Initial problem: variable input-output
Difficult for most machine learning (ML) models
Simplification: use ML for cuisine prediction, then get and extract best matching ingredients
Data:
Inputs: TF-IDF vectorized partial list of ingredients
Outputs: cuisine -> missing ingredients

4. Methodology Neural Network: Post-Processing:
Input: TF-IDF vectorized list of ingredients
Hidden: 300 nodes
Hidden: 100 nodes
Output: Number of missing ingredients
Post-Processing:
Given predicted cuisine, retrieve most common ingredients
Recommend by density of main ingredients (e.g. chicken legs, carrots) and spices (e.g. salt, vegetable oil)

5. Results
Neural network performance:
Algorithm performance:
71.6%
62.4%

6. Discussion Neural network likely overfits
Post-processing technique not powerful enough to catch unique cases
Future improvements:
Look into better neural network structure and dropout to combat overfitting
Investigate better methods of encoding ingredients (or similar data) for prediction