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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:

• Asking a user to rate an item on a sliding scale.
• Asking a user to rank a collection of items from favorite to least favorite.
• Presenting two items to a user and asking him/her to choose the best one.
• Asking a user to create a list of items that he/she likes.

Examples of implicit data collection include the following:

• Observing the items that a user views in an online store.
• Analyzing item/user viewing times[1]
• Keeping a record of the items that a user purchases online.
• Obtaining a list of items that a user has listened to or watched on his/her computer.
• Analyzing the user's social network and discovering similar likes and dislikes

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.[2] Herlocker provides an overview of evaluation techniques for recommender systems.[3]

More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference[4] as opposed to the Collaborative Filtering techniques of eCommerce. Redemption rates, or "hit rates," are much higher averaging as much as 45% in chain grocery stores.

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data.

Recommender systems are also sometimes known colloquially as "Gilligans".

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