Recommendation system
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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.
Recommendation systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves.
See also
External links
- Toward the Next Generation of Recommender Systems (http://dx.doi.org/10.1109/TKDE.2005.99) (DOI: 10.1109/TKDE.2005.99)
- KindaKarma (http://www.kindakarma.com)