In recommendation systems, there is always a trade-off between recommending items that fall into the user's sweet spot, based on what we already know about the user (exploitation), and recommending items that don't fall into the user's sweet spot, with the aim to expose the user to some novelties (exploration). Recommendation systems with little exploration will only recommend items that are consistent with the previous user ratings, thus preventing showing items outside of their current bubble. In practice, the serendipity of getting new items out of the user's sweet spot is often desirable, leading to a pleasant surprise, and potentially, the discovery of new sweet spots.

In this section, we discussed the essential concepts required to start building recommendation engines. Now, let's take a look at how to actually build one with Apache Mahout.