Recommendation systems are programs which attempt to predict items (movies, music, books, news, web pages) that a user may be interested in, given some information about the user's profile. Often, this is implemented as a collaborative filtering algorithm.

Recommendation systems work by collecting data from users, using a combination of explicit and implicit methods.

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.
• 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.

The recommendation 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.

Recommendation 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.

See also

• Pandora (music service)
• Collaborative filtering
• Collective intelligence
• The Long Tail
• Personalized marketing
• Product Finders
• Preference elicitation

Electronic commerce | Marketing | Information systems

External links

• Toward the Next Generation of Recommender Systems (DOI: 10.1109/TKDE.2005.99)

Recommendation System | מערכות המלצה | Рекомендательная система