Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user. Typically, a recommender system compares the user's 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).
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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]
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 Nearest Neighborhood approach.[4]. 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. 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.
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:[5]
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.
See also Netflix Prize.
See also
- Cold start
- Collaborative filtering
- Collective intelligence
- Enterprise bookmarking
- Personalized marketing
- Preference elicitation
- Product Finders
- The Long Tail
- Slope One
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, doi:, 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, doi:, http://portal.acm.org/citation.cfm?id=963772.
- ^ 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.
- ^ R. Bell, Y. Koren, C. Volinsky (2007). ""The BellKor solution to the Netflix Prize"". http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf.
Further reading
- Perkpipe Official Blog, "Making our social web a better place", Oct 17, 2009.
- Hangartner, Rick, "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". IEEE Spectrum. http://www.spectrum.ieee.org/may09/8788.
- Li, Q., Myaeng, S. H., and Kim, B. M. 2007. A probabilistic music recommender considering user opinions and audio features. Inf. Process. Manage. 43, 2 (Mar. 2007), 473-487. DOI= http://dx.doi.org/10.1016/j.ipm.2006.07.005
External links
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- Collection of research papers
- Content-Boosted Collaborative Filtering for Improved Recommendations. Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
- Methods and Metrics for Cold-Start Recommendations Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock.PDF (126 KiB)
Research groups
- GroupLens
- IFI DBIS Next Generation Recommender Systems
- IISM
- Univ. of Southampton IAM Group
- CoFE
- Duine Recommender Framework
- LIBRA
- Intelligent Systems and Business Informatics research group at University Klagenfurt, Austria
- Univ. of Fribourg Statistical Physics Group
- Laboratory for Web Science, FFHS, Switzerland
Workshops
- ECAI 2008 Workshop on 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
ACM Recommender Systems Series
Journal special issues
- ACM Transactions on the Web Special issue on Recommenders on the Web
- AI Communications Special issue on Recommender Systems
- 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)
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