Recommender system
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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).
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[edit] 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 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. Adomavicius provides an overview of recommender systems.[2] Herlocker provides an overview of evaluation techniques for recommender systems.[3]
More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference[4] 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".
[edit] Algorithms
One of the most commonly used algorithms in recommender systems is Nearest Neighborhood approach.[5]. 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.
[edit] Examples
- Aggregate Knowledge (recommendations and discovery)
- Baynote (recommendation web service)
- Clicktorch Intelligent Product and Content Recommendation System
- ConfigWorks (interactive selling solutions)
- Criteo (recommendation technology)
- Criticker (film recommendation engine)
- Daily Me (news recommendation system (hypothetical))
- Foodio54 (restaurant recommender service)
- FreshNotes (recommendation engine)
- Heeii (recommendation plugin)
- iGoDigital(recommendation engine)
- inSuggest (recommendation engine)
- iLike (music service)
- Last.fm (music service)
- Minekey (recommendation web service)
- MyStrands (developer of social recommendation technologies)
- Pandora (music service)
- Peerius (recommendations and discovery)
- prudsys RE (recommendation system)
- Photoree (image recommender system)
- Slacker (music service)
- StumbleUpon (web discovery service)
[edit] See also
- Cold start
- Collaborative filtering
- Collective intelligence
- Personalized marketing
- Preference elicitation
- Product Finders
- The Long Tail
[edit] References
- ^ Parsons, 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.
- ^ 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, J. L.; Konstan, J. A.; Terveen, L. G. & Riedl, J. T. (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>.
- ^ Quatse, Jesse and Najmi, Amir (2007) "Empirical Bayesian Targeting," Proceedings, WORLDCOMP'07, World Congress in Computer Science, Computer Engineering, and Applied Computing.
- ^ Sarwar, B.; Karypis, G.; Konstan, J. & Riedl, J. (2000), Application of Dimensionality Reduction in Recommender System A Case Study, <http://glaros.dtc.umn.edu/gkhome/node/122>.
[edit] External links
- Clicktorch Intelligent Product and Content Recommendation System)
- 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.
- Web's largest collection of scientific literatur about recommender systems
[edit] 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
[edit] ACM Recommender Systems Series
- RecSys 2008
- RecSys 2007: home page, proceedings
- Recommenders06: Summer School on The Present and Future of Recommender Systems
[edit] 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)
[edit] Workshops
- WI'08 Workshop on Web Personalization, Reputation and Recommender Systems
- ICADIWT 2008 - First International Workshop on Recommender Systems and Personalized Retrieval
- ECAI'08 - Workshop on Recommender Systems
- ReColl'08 - International Workshop on Recommendation and Collaboration
- AAAI'07 Workshop on Recommender Systems in e-Commerce
- WI'07 Workshop on Web Personalization and Recommender Systems
- 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
[edit] Further reading
- Hangartner, Rick, "What is the Recommender Industry?", MSearchGroove, December 17, 2007.
