drboli / recommendation_system_ibm

Recommendation_System_IBM

Project Overview

This project analyzes the interactions between users and articles on the IBM Watson Studio platform and makes recommendations to users about new articles. Three recommendation methods were explored: Rank Based Recommendation, User-User Based Collaborative Filtering and Matrix Factorization using Singular Value Decomposition (SVD).

1. Table of Contents
2. File Descriptions
3. Contents
4. Licensing, Authors, and Acknowledgements

File descriptions

Recommendations_with_IBM.ipynb: Jupyter notebook containing main implementation and analysis.

data/user-item-interactions.csv: Csv file with user-item interactions.

data/articles_community.csv: Csv file with indexed items.

Contents

The project is organized along the following steps:

1. Exploratory Data Analysis
2. Rank Based Recommendations
3. User-User Based Collaborative Filtering
4. Matrix Factorization using Singular Value Decomposition (SVD)

Licensing, Authors, and Acknowledgements

The data were provided by IBM as part of Udacity Data Science Nanodegree program.