A recommendation engine, also known as a recommender system, is software that analyzes available data to make suggestions for something that a website user might be interested in, such as a book, a video or a job, among other possibilities.
An engine, in a software context, is a special-purpose program that performs a task through a variable algorithm, often as a feature of some larger program. A search engine is one type of recommendation engine, responding to search queries with pages of results that are (at least theoretically) the search engine's best suggestions for websites that satisfy the user's query, based on the search term plus other data, such as location and trending topics.
Recommendation engines are common among e-commerce, social media and content-based websites. Amazon was one of the first sites to use a recommendation system. When the company was essentially an online book store, it began using software to suggest books the user might be interested in, based on data gathered about their previous activity, as well as the activity of other users who made similar choices.
Recommendation engines use a variety of technologies and techniques that enable them to filter large amounts of data and provide a smaller, focused body of suggestions for the user. Netflix, for example, uses metadata tagging on videos in conjunction with data about user behavior to come up with recommended movies and TV shows for specific members. LinkedIn uses the semi-structured data provided by members, including things like locations, job titles, skill sets and industries, to fuel their "Jobs you might be interested in" section.
Anmol Bhasin describes two types of recommendation engines: