The goal of this project is to build content-based recommender system using NLP. The recipes of AFA is a recommendation system with content-based filtering methods to generate recipes that have something in common in terms of the ingredients.
The dataset contains around 40,000 recipes scrapped from Allrecpies.com (Kaggle). Each recipe entry contains a recipe title, a list of ingredients and measurements, instructions for preparation and a picture of the final result.
1- Data Preprocessing:
Various preprocessing steps were used such as: tokenization, remove punctuation mark and digits and remove stop words that were created based on the proposed model.
2 - Vectorization : Count Vectorizer and TF-IDF Vectorizer
3 - Matrix factorization: LSA and NMF
4 - Cosine similarity
- Numpy and Pandas for data manipulation
- Scikit-learn for modeling
- Matplotlib and Seaborn for plotting
- NLTK and Gensim for topic modeling