Recommender-System-with-IBM
Introduction
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. Within this scope, I aimd to created a recommendation system to enhance the user experience and connect them with assets. This personalizes the experience for each user.
In this project I analyzed the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles you think they will like.
Libraries used
Libraies in this project includes numpy, pandas, matplotlibs, pickle.
Motivation for the project
Exploratory Data Analysis
Dive into the dataset before making recommendations of any kind to understand the data better.
Rank Based Recommendations
Find the most popular articles simply based on the most interactions.
User-User Based Collaborative Filtering
Look at users that are similar in terms of the items they have interacted with. These items could then be recommended to the similar users. This would be a step in the right direction towards more personal recommendations for the users.
Matrix Factorization
Using the user-item interactions to build out a matrix decomposition.
Files in the repository
Recommendations_with_IBM.ipynb is the jupyter notebook that contains the code. articles_community.csv and user-item-interactions.csv are the original datasets. project_tests.py is the test file.
Acknowledgement
This is a project from Udacity.