In the IBM Watson Studio, there is a large collaborative community ecosystem of articles, datasets, notebooks, and other A.I. and ML. assets. Users of the system interact with all of this.

This is a recommendation system project to enhance the user experience and connect them with assets to improve user engagement. This personalizes the experience for each user. Interactions that users have with articles on the IBM Watson Studio platform are analyzed and new article recommendations are made to them which they will probably like.

The jupyter notebook has the following contents:

1. Data Exploration
2. Rank Based Recommendations
3. User-User Collaborative filtering
4. Matrix Factorization

You can either open the Recommendations_with_IBM_pub.html file

or

Recommendations_with_IBM_pub.ipynb for jupyter notebook

1. From command line go to the folder by typing cd /Recommendations_with_IBM

2. You can start the notebook server from the same command line (using Terminal on Mac/Linux, Command Prompt on Windows) by running:

jupyter notebook

3. Open Recommendations_with_IBM_pub.ipynb file

MIT