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A recommendation engine is a system that identifies and provides recommended content or digital items for users. As mobile apps and other advances in technology continue to change the way users choose and utilize information, the recommendation engine is becoming an integral part of applications and software products.
Recommendation engines are also known as recommendation systems.
One popular example of a recommendation engine is the algorithmic software built into music applications like Pandora, Spotify and others. Previously, users downloaded specific tracks from digital stores and put them into their mobile devices. The use of a recommendation engine is part of what is now powering a major change in listening: instead of buying and playing individual tracks, users can sign onto a system that plays them selected tracks, based on their general listening preferences.
In building a recommendation engine, designers can use collaborative filtering that accesses past behavior or other metrics. Generally, a recommendation engine represents part of the new field of machine learning, where designers create software that attempts to learn more about the user and builds a user profile to deliver tailored, personal results. This is similar to what happens in natural language processing systems, where the software "learns" how an individual user speaks over time.
As the recommendation engine evolves, developers and engineers are looking at how to make these types of algorithmic software more effective. This represents part of the field of artificial intelligence where computers and software systems seem to become more capable of interacting with humans in an intelligent way, in this case, by knowing what they want and offering it to them on a regular basis.