Recommender_system

Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user\'s profile 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).

Overview

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 timesParsons, J.; Ralph, P. & Gallagher, K. (July 2004), Using viewing time to infer user preference in recommender systems., AAAI Workshop in Semantic Web Personalization, San Jose, California.
• 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. Adomavicius provides an overview of recommender systems.Adomavicius, G. & Tuzhilin, A. (June 2005), "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering 17 (6): 734–749, ISSN 1041-4347, doi:10.1109/TKDE.2005.99, <http://portal.acm.org/citation.cfm?id=1070611.1070751>. Herlocker provides an overview of evaluation techniques for recommender systems.Herlocker, J. L.; Konstan, J. A. & Terveen, L. G. et al. (January 2004), "Evaluating collaborative filtering recommender systems", ACM Trans. Inf. Syst. 22 (1): 5–53, ISSN 1046-8188, doi:10.1145/963770.963772, <http://portal.acm.org/citation.cfm?id=963772>.

More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inferenceQuatse, Jesse and Najmi, Amir (2007) "Empirical Bayesian Targeting," Proceedings, WORLDCOMP\'07, World Congress in Computer Science, Computer Engineering, and Applied Computing. 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.

Recommender systems are also sometimes known colloquially as "Gilligans".

Algorithms

One of the most commonly used algorithms in recommender systems is Nearest Neighborhood approach.Sarwar, B.; Karypis, G. & Konstan, J. et al. (2000), Application of Dimensionality Reduction in Recommender System A Case Study, <http://glaros.dtc.umn.edu/gkhome/node/122>.. In a social network, a particular user\'s neighborhood with similar taste or interest can be found by calculating Pearson Correlation, by collecting the preference data of top-N nearest neighbors of the particular user, the user\'s preference can be predicted by calculating the data using certain techniques.

Examples

• Aggregate Knowledge (recommendations and discovery)
• Baynote (recommendation web service)
• Heeii / Heeii (recommendation plugin)
• Last.fm (music service)
• iLike (music service)
• Minekey (recommendation web service)
• MyStrands (developer of social recommendation technologies)
• Pandora (music service)
• Peerius - Peerius (recommendations and discovery)
• prudsys RE (recommender system based on reinforcement learning)
• Photoree (image recommender system)
• Slacker
• StumbleUpon (web discovery service)
• ConfigWorks www.configworks.com (interactive selling solutions)

See also

• Cold start
• Collaborative filtering
• Collective intelligence
• Personalized marketing
• Preference elicitation
• Product Finders
• The Long Tail

References

External links

• Peerius (Recommendation and discovery technology)
• Photoree (image recommendation system)
• prudsys (recommender systems based on reinforcement learning technologies)
• Like-I-Like.org (movie recommendation web service)
• MIT-CCI wiki on Computer supported collaborative work perspective on collective intelligence
• 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
• Methods and Metrics for Cold-Start RecommendationsPDF (126 KiB)
• Recommended Systems Resource Center
• Localina (location recommendation system)
• Interview: Recommendations 2.0 by John Riedl, Ph.D.

Research Groups

• GroupLens
• IFI DBIS Next Generation Recommender Systems
• IISM
• Univ. of Southampton IAM Group
• CoFE
• Duine
• LIBRA
• Intelligent Systems and Business Informatics research group at University Klagenfurt, Austria

ACM RecSys Series

• ACM Recommender Systems (RecSys 2007): home page, proceedings
• Recommenders06: Summer School on The Present and Future of Recommender Systems

Journal Special Issues

• AI Communications Special issue on Recommender Systems: call for papers
• IEEE Intelligent Systems Special Issue on Recommender Systems, Vol. 22(3), 2007
• International Journal of Electronic Commerce Special Issue on Recommender Systems, Volume 11, Number 2 (Winter 2006-07)
• ACM Transactions on Computer-Human Interaction (TOCHI) Special Section on Recommender Systems Volume 12, Issue 3 (September 2005)
• ACM Transactions on Information Systems (TOIS) Special Issue on Recommender Systems, Volume 22, Issue 1 (January 2004)
• Journal of Information Technology and Tourism Special issue on Recommender Systems, Volume 6, Number 3 (2003)
• Communications of the ACM Special issue on Recommender Systems, Volume 40, Issue 3 (March 1997)

Workshops

• ECAI\'08 - Workshop on Recommender Systems
• ReColl\'08 - International Workshop on Recommendation and Collaboration
• AAAI\'07 Workshop on Recommender Systems in e-Commerce
• ECAI 2006 Workshop on Recommender Systems
• ACM SIGIR 2001 Workshop on Recommender Systems
• ACM SIGIR \'99 Workshop on Recommender Systems
• CHI\' 99 Workshop Interacting with Recommender Systems

Further reading

• Hangartner, Rick, "What is the Recommender Industry?", MSearchGroove, December 17, 2007.

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