40 Algorithms Every Programmer Should Know

Collaborative filtering recommendation engines

The recommendations from collaborative filtering are based on the analysis of the historical buying patterns of users. The basic assumption is that if two users show interest in mostly the same items, we can classify both users as similar. In other words, we can assume the following:

• If the overlap in the buying history of two users exceeds a threshold, we can classify them as similar users.

• Looking at the history of similar users, the items that do not overlap in the buying history become the basis of future recommendations through collaborative filtering.

For example, let's look at a specific example. We have two users, Mike and Elena, as shown in the following diagram:

Note the following: ...