Recommender system
From Wikipedia, the free encyclopedia
Recommender systems are computer 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.
Recommender 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.
- 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.
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.
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.
[edit] References
- ^ Parsons, J., Ralph, P., & Gallagher K. (2004). Using viewing time to infer user preference in recommender systems. AAAI Workshop in Semantic Web Personalization, San Jose, California, July.
[edit] See also
- Pandora (music service)
- Collaborative filtering
- Collective intelligence
- The Long Tail
- Personalized marketing
- Product Finders
- Preference elicitation
