RECOMMENDER SYSTEM

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'Recommender systems' are a specific type of information filtering (IF) technique that attempt to present to the user information items (movies, music, books, news, web pages) the user is interested in. To do this the user's profile is compared to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach).
When building the user's profile a distinction is made between explicit and implicit forms of data collection.
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.

★ Analyzing the user's social network and discovering similar likes and dislikes
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.
More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference^[2] as opposed to the Collaborative Filtering techniques of eCommerce. Redemption rates, or "hit rates," are much higher averaging as much as 45% in chain grocery stores.
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.

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References

See also

External links

References

1. 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.
2. Quatse, Jesse and Najmi, Amir (2007) "Empirical Bayesian Targeting," Proceedings, WORLDCOMP'07, World Congress in Computer Science, Computer Engineering, and Applied Computing

See also

★ Last.fm (music service)

★ Pandora (music service)

★ Musicovery (music service)

★ StumbleUpon (web discovery service)

★ Music-Map (music service)

★ Collaborative filtering

★ Collective intelligence

★ The Long Tail

★ Personalized marketing

★ Product Finders

★ Preference elicitation

★ Baynote (recommendation web service)

★ Minekey (recommendation web service)

External links

★ Collection of research papers

★ Word of Mouth: The Marketing Power of Collaborative Filtering

★ Content-Boosted Collaborative Filtering for Improved Recommendations. Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan

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

★ Methods and Metrics for Cold-Start Recommendations