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Recommended Systems Resource Center


Recommender Systems Course Syllabi
Stanford University CS345 Syllabus
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.
DePaul University Syllabus
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.
University of Michigan Syllabus
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.

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Update :: December 15, 2019