Recommendations-with-IBM

Introduction

This project focuses on developing recommendation engines that suggest new articles to users based on their interactions within the IBM Watson Studio platform. The engines employ three different techniques: knowledge-based filtering, collaborative filtering, and content-based filtering. Further details can be found in this article on medium.

File Description

articles_community.csv: This dataset contains records of user-article interactions
user-item-interactions.csv: This dataset provides information about each article
recommender_functions.py: This python script contains functions called by recommender.py
recommender.py: This Python script serves as the main orchestrator of the recommendation engines' functionality. The file also includes demo codes at the end

Instruction

To see a demonstration of the recommendation engines, run the recommender.py script.

Demo outputs

Acknowledgements

Udacity for designing the project
IBM for providing datasets