Chapter 2. Introducing recommenders
This chapter covers
- What recommenders are, within Mahout
- A first look at a recommender in action
- Evaluating the accuracy and quality of recommender engines
- Evaluating a recommender on a real data set: GroupLens
Each day we form opinions about things we like, don’t like, and don’t even care about. It happens unconsciously. You hear a song on the radio and either notice it because it’s catchy, or because it sounds awful—or maybe you don’t notice it at all. The same thing happens with T-shirts, salads, hairstyles, ski resorts, faces, and television shows.
Although people’s tastes vary, they do follow patterns. People tend to like things that are similar to other things they like. Because Sean loves bacon-lettuce-and-tomato sandwiches, you can guess that he would enjoy a club sandwich, which is mostly the same sandwich but with turkey. Likewise, people tend to like things that similar people like.
These patterns can be used to predict such likes and dislikes. Recommendation is all about predicting these patterns of taste, and using them to discover new and desirable things you didn’t already know about.
After introducing the idea of recommendation in more depth, this chapter will help you experiment with some Mahout code to run a simple recommender engine and understand how well it works, in order to give you an immediate feel for how Mahout works in this regard.