rec om mender system

8/8/2019 Rec Om Mender System

http://slidepdf.com/reader/full/rec-om-mender-system 1/6

Recommender systemFrom Wikipedia, the free encyclopedia

Recommender systems, recommendation systems, recommendation engines, recommendation

frameworks, recommendation platforms or simply recommender form or work from a specific type of

information filtering system technique that attempts to recommend information items (movies, TV

program/show/episode, video on demand, music[1], books, news, images, web pages, scientific literature such as

research papers etc.) that are likely to be of interest to the user.

Typically, a recommender system compares a user profile to some reference characteristics, and seeks to predict

the 'rating' that a user would give to an item they had not yet considered. These characteristics may be from the

information item (the content-based approach) or the user's social environment (the collaborative filtering

approach).

Contents

1 Overview2 Algorithms3 Recommendation search engines4 See also

5 References6 Further reading

7 External links7.1 Research groups7.2 Workshops7.3 ACM Recommender Systems Series

7.3.1 Journal special issues7.3.2 Books

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[2]

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

mmender system - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Recommend

12/22/2010 1



The recommender system compares the collected data to similar and not 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. Montaner provides the first overview of recommender systems,

from an intelligent agents perspective.[3] Adomavicius provides a new overview of recommender systems.[4]

Herlocker provides an overview of evaluation techniques for recommender systems.[5]

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.

Algorithms

One of the most commonly used algorithms in recommender systems is the k-nearest neighborhood

approach.[6]. 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 (weighted by similarity), the user's preference can be predicted by calculating the data using certain

techniques.

Another family of algorithms that is widely used in recommender systems is collaborative filtering. Collaborative

filter methods are based on collecting and analysing a large amount of information on users’ behaviour, activity

or preferences and predicting what users will like based on their similarity to other users.[7] One of the most

common types of Collaborative Filtering is item-to-item collaborative filtering (people who buy x also buy y), an

algorithm popularized by Amazon.com's recommender system. User-based collaborative filtering attempts to

model the social process of asking a friend for a recommendation. A particular type of collaborative filtering

algorithms uses matrix factorization, a low-rank matrix approximation techique[8][9]. A key advantage of the

collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable

of accurately recommending complex items such as movies without requiring an "understanding" of the item

itself.

Building user profiles using collaborative filtering can be problematic from a privacy point of view. Many

European countries have a strong culture of data privacy and every attempt to introduce any level of user

profiling can result in a negative customer response.[10]

The Netflix Prize, a contest with a dataset of over 100 million movie ratings and a grand prize of $1,000,000,

has energized the search for new and more accurate algorithms. The most accurate algorithm in 2007 used 107

different algorithmic approaches, blended into a single prediction:[11]

Predictive accuracy is substantially improved when blending multiple predictors. Our experience is

that most efforts should be concentrated in deriving substantially different approaches, rather than

refining a single technique. Consequently, our solution is an ensemble of many methods.

Recommendation search engines

MeeMix is a music recommendation engine.

Taboola is a personalized video recommendation engine used by sites like CNN, The NYTimes,Bloomberg, Slate, Demand Media, founded by Adam SingoldaHunch is a personalized recommendation engine supporting multiple product categories, co-founded byCaterina Fake of Flickr fame


12/22/2010 1



Freebase movies, music, and television blinkx video on demand

Boxee features a recommender system and social network platform for movies, music, television, and webvideo streams.The Filter entertainment and informationIMDb movies

MovieLens moviesJinni movies and television

Rotten Tomatoes moviesClicker.com televisionTrak televisionStrands Recommender General purpose SaaS recommender platform / API

TV Genius is a personalized TV recommendation engine designed for TV, web, and mobile.Flixster moviesMusicBrainz musicGravity Technologies retail, movies and televisionTank Top TV online television

Last.fm musicLibre.fm music

Genieo news stories, blog postsPing music, is Apple's recommender system and social network platform for iTunesintroAnalytics.com online dating, social media and e-commerce recommender systemSugestio (http://www.sugestio.com) General purpose SaaS recommender platform / API developed at

Ghent UniversityEasyrec General purpose Open-Source SaaS recommender system / APIScarab Cloud (http://www.scarabresearch.com) scientific SaaS e-commerce product recommender system

See also

Rating siteCold startCollaborative filteringCollective intelligenceContent Discovery PlatformEnterprise bookmarking

Netflix PrizePersonalized marketingPreference elicitationProduct finders

The Long Tail

Slope One

References

^ How Computers Know What We Want — Before We Do (http://www.time.com/time/magazine/article

/0,9171,1992403,00.html)

1.

^ 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.

2.

^ Montaner, M.; Lopez, B.; de la Rosa, J. L. (June 2003), "A Taxonomy of Recommender Agents on theInternet" (http://www.springerlink.com/content/kk844421t5466k35/) , Artificial Intelligence Review 19

3.


12/22/2010 1



(4): 285–330, doi:10.1023/A:1022850703159 (http://dx.doi.org/10.1023%2FA%3A1022850703159) ,http://www.springerlink.com/content/kk844421t5466k35/.

^ Adomavicius, G.; Tuzhilin, A. (June 2005), "Toward the Next Generation of Recommender Systems: ASurvey of the State-of-the-Art and Possible Extensions" (http://portal.acm.org/citation.cfm?id=1070611.1070751) , IEEE Transactions on Knowledge and Data Engineering 17 (6):734–749, doi:10.1109/TKDE.2005.99 (http://dx.doi.org/10.1109%2FTKDE.2005.99) ,

http://portal.acm.org/citation.cfm?id=1070611.1070751.

4.

^ Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (January 2004), "Evaluating collaborative

filtering recommender systems" (http://portal.acm.org/citation.cfm?id=963772) , ACM Trans. Inf. Syst. 22(1): 5–53, doi:10.1145/963770.963772 (http://dx.doi.org/10.1145%2F963770.963772) ,http://portal.acm.org/citation.cfm?id=963772.

5.

^ 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) ,http://glaros.dtc.umn.edu/gkhome/node/122.

6.

^ http://www.tvgenius.net/resources/white-papers/an-integrated-approach-to-tv-recommendations/7.

^ Takács, G.; Pilászy, I.; Németh, B.; Tikk, D. (March 2009), "Scalable Collaborative FilteringApproaches for Large Recommender Systems" (http://www.jmlr.org/papers/volume10/takacs09a

/takacs09a.pdf) , Journal of Machine Learning Research 10: 623–656, http://www.jmlr.org/papers/volume10/takacs09a/takacs09a.pdf

8.

^ Rennie, J.; Srebro, N. (2005). "Fast Maximum Margin Matrix Factorization for CollaborativePrediction" (http://people.csail.mit.edu/jrennie/papers/icml05-mmmf.pdf) . In Luc De Raedt, StefanWrobel (PDF). Proceedings of the 22nd Annual International Conference on Machine Learning . ACMPress. http://people.csail.mit.edu/jrennie/papers/icml05-mmmf.pdf.

9.

^ http://www.tvgenius.net/resources/white-papers/an-integrated-approach-to-tv-recommendations/10.

^ R. Bell, Y. Koren, C. Volinsky (2007). ""The BellKor solution to the Netflix Prize""(http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf) . http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf.

11.

Further reading

Hangartner, Rick, "What is the Recommender Industry?" (http://www.msearchgroove.com/2007/12/17/guest-column-what-is-the-recommender-industry/) , MSearchGroove, December 17, 2007.Robert M. Bell, Jim Bennett, Yehuda Koren, and Chris Volinsky (May 2009). "The Million Dollar Programming Prize" (http://www.spectrum.ieee.org/may09/8788) . IEEE Spectrum .http://www.spectrum.ieee.org/may09/8788.

External links

Content-Boosted Collaborative Filtering for Improved Recommendations. Prem Melville, Raymond J.

Mooney, and Ramadass Nagarajan (http://www.cs.utexas.edu/users/ml/publication/paper.cgi?paper=cbcf-aaai-02.ps.gz)

Collection of research papers (http://www.andreas-ittner.de/index_rs.html)Methods and Metrics for Cold-Start Recommendations Andrew I. Schein, Alexandrin Popescul, Lyle H.Ungar, David M. Pennock. (http://www.cis.upenn.edu/datamining/Publications/p8734-schein.pdf)PDF (126 KiB)

Research groups

GroupLens (http://www.grouplens.org/)


12/22/2010 1



IEEE Intelligent Systems Special Issue on Recommender Systems, Vol. 22(3), 2007(http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/mags/ex/&toc=comp/mags/ex

/2007/03/x3toc.xml)International Journal of Electronic Commerce Special Issue on Recommender Systems, Volume 11, Number 2 (Winter 2006-07) (http://portal.acm.org/toc.cfm?id=1278152)ACM Transactions on Computer-Human Interaction (TOCHI) Special Section on Recommender Systems

Volume 12, Issue 3 (September 2005) (http://portal.acm.org/citation.cfm?id=1096737.1096738)ACM Transactions on Information Systems (TOIS) Special Issue on Recommender Systems, Volume 22,

Issue 1 (January 2004) (http://portal.acm.org/toc.cfm?id=963770)Journal of Information Technology and Tourism Special issue on Recommender Systems, Volume 6, Number 3 (2003) (http://tourism.wu-wien.ac.at/Jitt/)Communications of the ACM Special issue on Recommender Systems, Volume 40, Issue 3 (March 1997)

(http://portal.acm.org/citation.cfm?id=245121)

Books

Recommender Systems An Introduction (http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=9780521493369)

Recommender Systems Handbook (http://www.springer.com/computer/ai/book/978-0-387-85819-7)Building Effective Recommender Systems (http://www.springer.com/computer/ai/book /978-1-4419-0047-0)

Retrieved from "http://en.wikipedia.org/wiki/Recommender_system"

Categories: Recommender systems

This page was last modified on 3 December 2010 at 10:38.Text is available under the Creative Commons Attribution-ShareAlike License; additional terms mayapply. See Terms of Use for details.

Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.


rec om mender system

Documents