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dc.contributor.advisorArif, Hossain
dc.contributor.authorOrnab, Ashique Mohaimin
dc.contributor.authorChowdhury, Sakia
dc.contributor.authorToa, Seevieta Biswas
dc.date.accessioned2018-02-20T09:17:10Z
dc.date.available2018-02-20T09:17:10Z
dc.date.copyright2017
dc.date.issued12/26/2017
dc.identifier.otherID 13201080
dc.identifier.otherID 14101252
dc.identifier.otherID 14101003
dc.identifier.urihttp://hdl.handle.net/10361/9535
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 60-61).
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractThe purpose of this research is to study the different techniques that can be approached in order to build a recommendation system. Here, we have analyzed the different approaches between two different collaborative filtering algorithms in perspective of a food diet recommendation system. A food recommendation system that will help people to choose their daily meal just the way we select movies to watch from suggestions in Netflix or add a friend in Facebook when the suggestion pops up in our home page. Sometimes people get bored of having the same food items on regular basis hence in order to help them get rid out of this monotonous lifestyle, we have proposed a diet recommendation system. In this paper, we first give you some basic information about what recommendation system is, and then we talk about the two collaborative algorithms and finally tell you what kind of approaches we have used to build a diet recommendation system.en_US
dc.description.statementofresponsibilityAshique Mohaimin Ornab
dc.description.statementofresponsibilitySakia Chowdhury
dc.description.statementofresponsibilitySeevieta Biswas Toa
dc.format.extent61 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports 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.
dc.subjectCosine similaritiesen_US
dc.subjectMatrix factorizationen_US
dc.subjectALSen_US
dc.subjectRecommendation systemen_US
dc.titleAn empirical study of collaborative filtering algorithms for building a diet recommendation systemen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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