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 Paul >> Resource Centers >> Web 2.0 >> Recommender Systems >> Recommender Systems Course Syllabi |
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| Recommended Systems Resource Center | |
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| Recommender Systems Course Syllabi | |
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| | Stanford University CS345 Syllabus. "Data Mining," covers an introduction to web mining, association rules, page rank, topic-specific page rank, hits and spam, minhashing, locality-sensitive hashing, clustering, recommender systems, structured data extraction, virtual databases, online algorithms, search advertising and stream mining. | http://www.stanford.edu/class/cs345a/
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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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| | Syllabus for a seven-week Recommender Systems course at the University of Michigan. Topics include an introduction (the design space), algorithms (person-person, item-item, matrix methods and graph methods), anonymity and privacy, prediction evaluation methods, rating scales, implicit ratings, herding and deliberate manipulation, use of ratings and social fragmentation. | http://www.si.umich.edu/~presnick/courses/fall06/recommender_systems.htm
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| Update :: December 15, 2019 |
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