Building a recommendation system

Now, it would be worthwhile that you learn to build one by yourself. We will build a simple recommender system to recommend restaurants to a given user.

ML Studio includes three sample datasets, described as follows:

• Restaurant customer data: This is a set of metadata about customers, including demographics and preferences, for example, latitude, longitude, interest, and personality.

• Restaurant feature data: This is a set of metadata about restaurants and their features, such as food type, dining style, and location, for example, placeID, latitude, longitude, price.

• Restaurant ratings: This contains the ratings given by users to restaurants on a scale of 0 to 2. It contains the columns: userID, placeID, and rating.

Now, we will build a recommender that will recommend a given number of restaurants to a user (userID). To build a recommender perform the following steps:

1. Create a new experiment. In the Search box in the modules palette, type Restaurant. The preceding...