Article Recommendations on the IBM Watson Studio Platform

In this project I implemented different recommendation engines for users of the IBM Watson Studio platform.

Results

Implemented

1. Rank Based Recommendations: recommended the most popular articles based on the highest user interactions
2. User-User Based Collaborative Filtering: recommended unseen articles that were viewed by most similar users
3. Content Based Recommendations: recommended articles based on similarity of content
4. Matrix Factorization: performed SVD to predict articles a user might interact with

File Descriptions

You can find the results of the analysis in either html form or complete Jupyter Notebook:

• Recommendations_with_IBM.html
• Recommendations_with_IBM.ipynb

Acknowledgements

The project is part of the Udacity Data Science Nanodegree.