Item-Based Collobrative Filtering Recommender System

Recommender System on Nutritional Values for Different Food Items

The vast amount of data available today allows information enthusiasts with right skills to draw fruitful insights by inferences and patterns from data. These inferences and patterns give rise to the concept of Recommender Systems which play a major role in our day-to-day decisions in the form of various categories like “Recommended Items”, “You may also Like” or “People you may know” in fields of ecommerce and social media. We intend to extend the capabilities of Recommender system to find similarities and diversity in various food nutritional values and present them as alternative to the food being looked for.

Packages required:

1. Numpy
2. Pandas
3. Sklearn
4. Joblib
5. Flask
6. Flask_bootstrap
7. FuzzyWuzzy

To install packages: pip install <package_name>

To run the recommender system file:

1. Open cmd
2. Set working directory ~\RecommenderSystem\CodeBase
3. Run command: 'python recomimpl.py'
4. Run command 'set flask=app.py'
5. Run command 'Run flask'
6. Copy the IP at the end of the command lines to a browser