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 Resource Centers >> Web 2.0 >> Recommender Systems >> Recommender Systems and Collaborative Filtering |
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| Recommended Systems Resource Center | |
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| Recommender Systems and Collaborative Filtering | |
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| | Sample Chapter: "The Insider's Guide to Collaborative Filtering and Recommender Systems," from Word of Mouse: The Marketing Power of Collaborative Filtering, August 2002, by John Reidl, Joseph Konstan, and Eric Vrooman. Discusses a history of collaborative filtering, information retrieval, information filtering, computerized collaborative filtering, the role of today's marketer, recommender technology and interfaces, automated collaborative filtering technology, predictions, recommendations, tuning recommendations and predictions, complete list of recommenders, interfaces 9inputs and outputs), input types, output types, output delivery, and recommenders in action. | http://www.hachettebookgroupusa.com/books/43/0446530034/chapter_excerpt15571.html
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| | Download the latest version of CoFE (the COllaborative Filtering Engine)--a recommendation engine for collaborative filtering. Developed by the Intelligent Information Systems (IIS) research group at Oregon State University, CoFE is a free, open source server for the Java platform that anyone can use to set up a recommendation system. Features include individual items recommendations, top-N recommendations across all items, top-N recommendations based on one type of item. Site includes downloads, documentation, papers, support, news and more. | http://eecs.oregonstate.edu/iis/CoFE/
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| | DePaul University's syllabus for their course "Intelligent Information Retrieval." This course covers personalized search, relevance feedback, client-side and server-side agents for filtering information, web content mining, collaborative filtering and recommend er systems, and the use of clustering. Projects include building a search/retrieval system, building a web search engine, implementing a simple meta-search engine, implementation a web browsing assistant using clustering, designing a domain-specific search agent, implementing a web mail, news or web filtering system, design and enhance a user interface for a retrieval system, using HTML parsing, and building a simple recommend er system. | http://maya.cs.depaul.edu/~classes/ds575/project.html
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| | MovieLens is a recommender system that uses collaborative filtering to provide you with movie recommendations based on your personal references. You preferences are matched with the preferences of other users with similar movie preferences. Visit the site for a tour of the service. MovieLens is a project of GroupLens Research. | http://movielens.umn.edu/login
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| | Paper: "Item-based Collaborative Filtering Recommendation Algorithms," by Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, all of the GroupLens Research Group at the University of Minnesota. Topics include collaborative filtering based recommender systems, item-based collaborative filtering algorithm and experimental evaluation. | http://www10.org/cdrom/papers/519/
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| | ACM SIGIR'99 Workshop on Recommender Systems: Algorithms and Evaluation. Download the papers that were presented at the workshop including: "Memory-Based Weighted-Majority Prediction for Recommender Systems," by Joaquin Delgado and Naohiro Ishii; "Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes," by Dhruv Gupta, Mark Digiovanni, Hiro Narita, and Ken Goldberg; "Clustering Items for Collaborative Filtering," by Mark O'Conner and Jon Herlocker; "Combining Content-Based and Collaborative Filters in an Online Newspaper," by Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin; "Bayesian Mixed-Effect Models for Recommender Systems," by Michelle Condliff, David D. Lewis, David Madigan, and Christian Posse; "Content-Based Book Recommending Using Learning for Text Categorization." by Raymond Mooney and Loriene Roy; "Recommenders for Expertise Management," by Mark Ackerman, David McDonald, Wayne Lutters, and Jack Mauamatsu; "Recommending Web Documents Based on User Preferences," by Eric Glover, Steve Lawrence, Michael Grodon, William Birmingham, and C. Lee Giles. | http://www.cs.umbc.edu/~ian/sigir99-rec/
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| | "Trust in Recommender Systems" by John O'Donovan and Barry Smyth. Discusses defining trust, trust and reputation modeling on the semantic web, trust-based filtering and recommendation, computational models of trust, profile-level and item-level trust, trust-based recommendation, evaluation, building trust, recommendation error, winners and losers, trust, reliability or competence?, acquiring real-world feedback, trust and collaborative filtering robustness, and trust and recommendation explanation. | http://www.csi.ucd.ie/UserFiles/publications/1125325051774.pdf
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| | "Beyond Recommender Systems: Helping People Help Each Other," by Loren Terveen and Will Hill (AT&T Labs Research). Topics include an introduction to recommender systems, examples and concepts, a model of the recommendation process, issues for computational recommender systems, major types of recommender systems (content-based, recommendation support, social data mining and collaborative filtering), current challenges and new opportunities. | http://www.grouplens.org/papers/pdf/rec-sys-overview.pdf
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| | GroupLens Research is a group from the Department of Computer Science and Engineering at the University of Minnesota. These faculty and student researchers study Human-Computer Interaction and Computer Supported Cooperative work. Current research areas include recommender systems, collaborative filtering, online communities, local geographic information systems, mobile and ubiquitous technologies, information filtering, assistive technologies and home information management systems. Visit the site for research papers, project information, the GroupLens blog and more. | http://www.grouplens.org/
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| | Tutorial: "Amazon.com Recommendations: Item-to-Item Collaborative Filtering," by Greg Linden, Brent Smith and Jeremy Smith. Discusses recommendation algorithms, traditional collaborative filtering, cluster models, search-based methods, item-to-item collaborative filtering (how it works), and scalability. | http://www.win.tue.nl/~laroyo/2L340/resources/Amazon-Recommendations.pdf
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| | Paper: "Accurate is Not Always Good: How Accuracy Metrics Have Hurt Recommender Systems," by Sean M. McNee, John Riedl and Joseph A. Konstan, all from the GroupLens Research, Department of Computer Science and Engineering at the University of Minnesota. | http://www.grouplens.org/papers/pdf/mcnee-chi06-acc.pdf
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| | Whitepaper: "Evaluating Collaborative Filtering Recommender Systems" by Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. Discusses user tasks for recommender systems, selecting data sets for evaluation, live user experiments vs. offline analysis, synthesized vs. natural data sets, properties of data sets, past and current trends in data sets, accuracy metrics, beyond accuracy, and user evaluation. | http://web.engr.oregonstate.edu/~herlock/papers/eval_tois.pdf
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| | Wikipedia entry for collaborative filtering. Discusses the methodology, history, types (active filtering, passive filtering, and item-based filtering) and applications (for both commercial and non-commercial uses). Includes links to collaborative software products. | http://en.wikipedia.org/wiki/Collaborative_filtering
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| Update :: November 20, 2009 |
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