Chapter 9. A Recommender System

Recommender systems are common these days. You may not have noticed, but you might already be a user or receiver of such a system somewhere. Most of the well-performing e-commerce platforms use recommendation systems to recommend items to their users. When you see on the Amazon website that a book is recommended to you based on your earlier preferences, purchases, and browse history, Amazon is actually using such a recommendation system. Similarly, Netflix uses its recommendation system to suggest movies for you.

A recommender or recommendation system is used to recommend a product or information often based on user characteristics, preferences, history, and so on. So, a recommendation is always personalized.

Until recently, it was not so easy or straightforward to build a recommender, but Azure ML makes it really easy to build one as long as you have your data ready.

This chapter introduces you to the concept of recommendation systems and also the model available...

The Matchbox recommender

Microsoft has developed a large-scale recommender system based on a probabilistic model (Bayesian) called Matchbox. This model can learn about a user's preferences through observations made on how they rate items, such as movies, content, or other products. Based on those observations, it recommends new items to the users when requested.

Matchbox uses the available data for each user in the most efficient way possible. The learning algorithm it uses is designed specifically for big data. However, its main feature is that Matchbox takes advantage of metadata available for both users and items. This means that the things it learns about one user or item can be transferred across to other users or items.

You can find more information about the Matchbox model at the Microsoft Research project link.

Types of recommendations

The Matchbox recommender supports the building of four kinds of recommenders, which will include most of the scenarios. Let's take a look at the following...

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...

Summary

You started with gaining the basic knowledge about a recommender system. You then understood the Matchbox recommender that comes with ML Studio along with its components. You also explored different kinds of recommendations that you can make with it. Finally, you ended up building a simple recommendation system to recommend restaurants to a given user.

In the following chapter, you will explore how to extend your experiments beyond the capability of ML Studio by writing code in either R or Python.