Make recommendations based on the interaction users have with articles on IBM Watson Studio platform
We have user and article interaction data from IBM Watson Studio Platform. In this project, we use that data to make article recommendations for users. We have used different recommended systems like Rank based, Content based, Collaborative Filtering to make recommendations for users. Not only that, we also use SVD Matrix Factorization recommendation system and try to predict articles for unseen users.
The data set has 2 main datasets:
- user-item-interactions.csv: Has user and article interactions
- articles_community.csv: Has details about the articles
This project requires Python 3.6 and the following Python libraries installed:
- NumPy
- Pandas
- matplotlib
- linalg.svd
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select Python 3.x installer.
Thanks to Udacity for providing access to the IBM data used in this project and guidance