Defining and setting up recommender systems in DataRobot

DataRobot, due to its ability to extract features from images, audio, and text data, effectively manages the feature availability limitation of the content-based recommender systems. This, in addition to DataRobot's automated ML models' processes, means it is well positioned to leverage the advantages of the content-based approach while compensating for the feature-unavailability limitation of this approach. As described in the Technical requirements section, the dataset used for our example consists of three tables. This includes the user table (presenting profiles of the users), the book table (outlining characteristics of the books), and the rating table (containing user book ratings). Since we have one table describing the books, and another, the users, integrating these and the ratings sets the scene for the content-based recommender system. To do this, we employed Jupyter Notebook. Figure 10.1 presents the script...