For this project I will analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles I think they will like. Below you can see an example of what the dashboard could look like displaying articles on the IBM Watson Platform.
Though the above dashboard is just showing the newest articles, you could imagine having a recommendation board available here that shows the articles that are most pertinent to a specific user. In order to determine which articles to show to each user, I will be performing a study of the data available on the IBM Watson Studio platform.
I. Exploratory Data Analysis
Before making recommendations of any kind, I will need to explore the data I am working with for the project. Dive in to see what I can find. There are some basic, required questions to be answered about the data I am working with throughout the rest of the notebook. I will use this space to explore, before diving into the details of the recommendation system in the later sections.
II. Rank Based Recommendations
To get started in building recommendations, I 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. You will implement this next.
IV. Matrix Factorization
Finally, I will complete a machine learning approach to building recommendations. Using the user-item interactions, I will build out a matrix decomposition. Using the decomposition, you will get an idea of how well I can predict new articles an individual might interact with (spoiler alert - it isn't great). I will finally discuss which methods you might use moving forward, and how I might test how well the recommendations are working for engaging users.