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Recommender systems form a specific type of information filtering (IF) technique that attempts to...

Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics, and seeks to predict the 'rating' that a user would give to an item they had not yet considered. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). When building the user's profile a distinction is made between explicit and implicit forms of data collection. Examples of explicit data collection include the following: Examples of implicit data collection include the following: The recommender system compares the collected data to similar data collected from others and calculates a list of recommended items for the user. Several commercial and non-commercial examples are listed in the article on collaborative filtering systems. Adomavicius provides an overview of recommender systems. Herlocker provides an overview of evaluation techniques for recommender