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dc.contributor.advisorSprague, Nathan
dc.contributor.authorNayer, Benjamin H.
dc.descriptioniv, 60 p.en_US
dc.description.abstractRecommendation systems, and specifically collaborative filtering, are fairly new fields, which focus on automatically predicting how much a given user would value a given item. As more and more aspects of society are entering the digital realm, the value of such systems is increasing. Improvements to the underlying algorithms and concepts are still being discovered, and further research is being encouraged. Notably, Netflix recently issued a challenge to the collaborative filtering community, with a million-dollar prize offered for whomever could produce a significant improvement over their existing recommendation system. This paper explores the history of collaborative filtering, and many of the core concepts and methods. In addition, the progress of the winning teams from the Netflix Prize is tracked and their methods explored. Finally, there is a look ahead at potential challenges that those working in the field of collaborative filtering will have to overcome.en_US
dc.relation.ispartofSenior Individualized Projects. Computer Science.
dc.relation.ispartofKalamazoo College Computer Science Senior Individualized Projects Collection
dc.rightsU.S. copyright laws protect this material. Commercial use or distribution of this material is not permitted without prior written
dc.titleCollaborative Filtering and the Netflix Prizeen_US
KCollege.Access.ContactIf you are not a current Kalamazoo College student, faculty, or staff member, email to request access to this thesis.

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  • Computer Science Senior Integrated Projects [237]
    This collection includes Senior Integrated Projects (SIP's) completed in the Computer Science Department. Abstracts are generally available to the public, but PDF files are available only to current Kalamazoo College students, faculty, and staff.

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