Chapter 2. Making Recommendations
To begin the tour of collective intelligence, I’m going to show you ways to use the preferences of a group of people to make recommendations to other people. There are many applications for this type of information, such as making product recommendations for online shopping, suggesting interesting web sites, or helping people find music and movies. This chapter shows you how to build a system for finding people who share tastes and for making automatic recommendations based on things that other people like.
You’ve probably come across recommendation engines before when using an online shopping site like Amazon. Amazon tracks the purchasing habits of all its shoppers, and when you log onto the site, it uses this information to suggest products you might like. Amazon can even suggest movies you might like, even if you’ve only bought books from it before. Some online concert ticket agencies will look at the history of shows you’ve seen before and alert you to upcoming shows that might be of interest. Sites like reddit.com let you vote on links to other web sites and then use your votes to suggest other links you might find interesting.
From these examples, you can see that preferences can be collected in many different ways. Sometimes the data are items that people have purchased, and opinions about these items might be represented as yes/no votes or as ratings from one to five. In this chapter, we’ll look at different ways of representing these cases ...