For this project we will analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles we think they will like.
. Exploratory Data Analysis
Before making recommendations of any kind, we will need to explore the data you are working with for the project. Dive in to see what you can find. There are some basic, required questions to be answered about the data you are working with throughout the rest of the notebook.
II. Rank Based Recommendations
To get started in building recommendations, we will first find the most popular articles simply based on the most interactions. Since there are no ratings for any of the articles, it is easy to assume the articles with the most interactions are the most popular. These are then the articles we might recommend to new users (or anyone depending on what we know about them).
III. User-User Based Collaborative Filtering
In order to build better recommendations for the users of IBM's platform, we could 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.
IV. Matrix Factorization
Finally, we will complete a machine learning approach to building recommendations. Using the user-item interactions, you will build out a matrix decomposition.