Recommender System
This was our 6th Sem mini project in which we learned about different types of Recommender systems and implemented some of them with the help of different blogs and tutorials. Till now we have implemented two types
- Content Based Filtering
- Deep Learning
We also created a Knowledge Graph which can take in set of sentences and creates a knowledge graph out of them. You can find all the three notebooks in the Notebooks folder
How to use Recommender Systems Notebooks
Either you can download the Notebooks from this repository and install the dependencies by running
pip install requirements.txt
Or if you want directly run them, use these links to Kaggle and Colab Notebooks which can have the relevant data and steps already in-place to be run
Note: *The Knowledge Graph section in second notebook won't run due to package dependencies.
How to use Knowledge Graph Notebook
You will have to run this Notebook locally due to problems with its dependencies.
Install nltk locally and follow these steps to install Stanford Tagger, NER, Tokenizer and Parser
TODOs
- Creating a Flask application to use the reccomender system created using Content Based Filtering
- Create a Collaborative Filtering Recommender Systems
Contributors: