Recommender system

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).


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 [citation |last=Parsons |first=J. |last2=Ralph |first2=P. |last3=Gallagher |first3=K. |date=July 2004 |month=July |year=2004 |title=Using viewing time to infer user preference in recommender systems. |publisher=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. [citation |last=Adomavicius |first=G. |last2=Tuzhilin |first2=A. |url= |title=Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions |journal=IEEE Transactions on Knowledge and Data Engineering |volume=17 |issue=6 |date=June 2005 |month=June |year=2005 |pages=734–749 |doi=10.1109/TKDE.2005.99 |issn=1041-4347.] Herlocker provides an overview of evaluation techniques for recommender systems. [citation |last=Herlocker |first=J. L. |last2=Konstan |first2=J. A. |last3=Terveen |first3=L. G. |last4=Riedl |first4=J. T. |date=January 2004 |month=January |year=2004 |url= |title=Evaluating collaborative filtering recommender systems |journal=ACM Trans. Inf. Syst. |volume=22 |issue=1 |pages=5–53 |doi=10.1145/963770.963772 |issn=1046-8188.]

More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference [Quatse, 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".


One of the most commonly used algorithms in recommender systems is Nearest Neighborhood approach. [citation |last=Sarwar |first=B. |last2=Karypis |first2=G. |last3=Konstan |first3=J. |last4=Riedl |first4=J. |year=2000 |url= |title=Application of Dimensionality Reduction in Recommender System A Case Study.] . 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.


* [ Aggregate Knowledge] (recommendations and discovery)
* Baynote (recommendation web service)
* [ ChoiceStream] (recommendation service provider)
* [ Clicktorch] Intelligent Product and Content Recommendation System
* Collarity (media recommendation platform)
* [ 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)
* (music service)
* [ Loomia] (Social and Personalized Recommendations For Media, Content and Retail Sites)
* MeeMix (personalized music service)
* [ mufin] (provider of semantic audio recommendation technologies)
* MyStrands (developer of social recommendation technologies)
* Pandora (music service)
* [ Photoree] (image recommender system)
* [ prudsys RE] (recommendation system)
* Slacker (music service)
* StumbleUpon (web discovery service)
* StyleFeeder (personalized shopping search)
* [ Tastehood] (recommendation engine for movies, music, books and more)
* [ Taste Kid] (music, films and books recommendation engine)
* [ TasteVine] (wine)
* Zemanta (authoring-time suggestions service)
* [ IMHONET] (1st russian language recommendation service)

ee also

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


External links

* [ Clicktorch] Intelligent Product and Content Recommendation System)
* [ Photoree] (image recommendation system)
* [ commendo] (large scale recommender systems)
* [ prudsys] (recommender systems based on reinforcement learning technologies)
* [] (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]
* [ 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]
* [ MediaChoice] (Patented Recommendation System)

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]
* [ Netflix prize]
* [ Univ. of Fribourg Statistical Physics Group]

[ ACM Recommender Systems Series]

* [ RecSys 2008]
* RecSys 2007: [ home page] , [ proceedings]
* [ Recommenders06: Summer School on The Present and Future of Recommender Systems]

Journal Special Issues

* [ ACM Transactions on the Web Special issue on Recommenders on the Web]
* [ 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)]


* [ 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 ]

Further reading

*Hangartner, Rick, [ "What is the Recommender Industry?"] , MSearchGroove, December 17, 2007.

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