In this chapter, our task is to recommend users on online social networks based on shared connections. Our logic is that if two users have the same friends, they are highly similar and worth recommending to each other. We want our recommendations to be of high value. We can only recommend so many people before it becomes tedious, therefore we need to find recommendations that engage users.
To do this, we use the previous chapter's disambiguation model to find only users talking about Python as a programming language. In this chapter, we use the results from one data mining experiment as input into another data mining experiment. Once we have our Python programmers selected, we then use their friendships to find clusters of users that are highly similar to each other. The similarity between two users will be defined by how many friends they have in common. Our intuition will be that the more friends two people have in common, the more likely two people are to be friends...
