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. 数据挖掘研究院
Recommendation systems work by collecting data from users, using a combination of explicit and implicit methods.
Examples of explicit data collection include the following: 数据挖掘研究院
- 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: 数据挖掘研究院
- 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. 数据挖掘研究院
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
- Collaborative filtering
- Collective intelligence
- The Long Tail
- Personalized marketing
- Product Finders
