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Evaluating if an analysis of testresult could be used when using gradient boosted decision tree inrecommender systems.
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This essay is aimed for using as a template for future creations of recommender system. The main purpose of the study is to provide additional information that can be beneficial when creating recommendersystems and it’s an uprising topic in the field of data mining were it’simportant to analyze large collections of data and calculate patterns. Inthe beginning these systems were only useful for simpler tasks, but hasevolved with the help of this contest to a much more complex systemand this study will mainly focus on demonstrating leading methods forcreating such recommender systems. Firstly the methods used are moredetailed explained and some main concepts are brought forward, endingwith a description of the datasets that were released. The results fromthe winning team BellKor’s Pragmatic Chaos will demonstrate the difference between each year for corresponding method and will result in aconclusion that by a analyze of the testing on the data, some of the finalpredictors could be found by using this technique. This will further reducing the quantity of combinations needed for testing and if there is atime pressure or financial issue, this is a strong argument for using thisanalysis as a template for future creations of recommender systems.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186492OAI: diva2:927415
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2016-05-12Bibliographically approved

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