The entire principle of a recommender system, as we described before, can be generalized into a different kind of system that has many other business applications. Some people would call it a rules engine, which does some kind of sophisticated if-this-then-that matching and figures out what action to take at the other end of the decision tree. Other people may call it a pattern-matching system, which could be applied to any kind of pattern and tied to any kind of action. Most likely, graph databases such as Neo4j hold some characteristics of all of the above and provide you with an interesting infrastructural optimization that could serve well.
Before wrapping up this chapter, we would like to highlight some use cases that are extremely related to the recommender system use case. Let's go through some well-known sweet spot applications...
