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Recommendation System




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 Algorithm s 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.


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