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Regina Barzilay. Tran, Tuan, M. Eng (Tuan Minh) Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2017-01-12T18:18:39Z 2017-01-12T18:18:39Z 2016 2016 http://hdl.handle.net/1721.1/106385 967660262 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 37). In my thesis, I present the methods I use to predict NBA games using matrix factorization. Matrix factorization is popular through the Netflix recommendation problem, but in general, one can apply it to data that are best modeled as the result of pairwise interaction. My thesis contains three parts. First, I explain how I model NBA prediction as a matrix factorization problem and use the basic low-rank matrix factorization approach to discover structure in the data. I also explain some differences between using matrix factorization for NBA prediction versus that in the Netflix recommendation problem. Second, I use probabilistic matrix factorization (PMF) to incorporate the fact that when two teams play each other, the scores will be different each time. Lastly, I incorporate supplementary information such as the date of the game by combining multiple PMF problems using Gaussian process priors. I replace the scalar latent features with functions of this supplementary information to aid with prediction. by Tuan Tran. M.Eng. 37 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Predicting NBA games with matrix factorization Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3a54e516-2b57-4ffe-8ad0-5abaf9d10e8c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Predicting NBA games with matrix factorization</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Tran, Tuan, M. Eng (Tuan Minh) Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In my thesis, I present the methods I use to predict NBA games using matrix factorization. Matrix factorization is popular through the Netflix recommendation problem, but in general, one can apply it to data that are best modeled as the result of pairwise interaction. My thesis contains three parts. First, I explain how I model NBA prediction as a matrix factorization problem and use the basic low-rank matrix factorization approach to discover structure in the data. I also explain some differences between using matrix factorization for NBA prediction versus that in the Netflix recommendation problem. Second, I use probabilistic matrix factorization (PMF) to incorporate the fact that when two teams play each other, the scores will be different each time. Lastly, I incorporate supplementary information such as the date of the game by combining multiple PMF problems using Gaussian process priors. I replace the scalar latent features with functions of this supplementary information to aid with prediction.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>