An empirical study of collaborative filtering algorithms for building a diet recommendation system

Cataloged from PDF version of thesis report.

Bibliographic Details
Main Authors:	Ornab, Ashique Mohaimin, Chowdhury, Sakia, Toa, Seevieta Biswas
Other Authors:	Arif, Hossain
Format:	Thesis
Language:	English
Published:	BRAC University 2018
Subjects:	Cosine similarities Matrix factorization ALS Recommendation system
Online Access:	http://hdl.handle.net/10361/9535

id	10361-9535
record_format	dspace
spelling	10361-95352022-01-26T10:18:21Z An empirical study of collaborative filtering algorithms for building a diet recommendation system Ornab, Ashique Mohaimin Chowdhury, Sakia Toa, Seevieta Biswas Arif, Hossain Department of Computer Science and Engineering, BRAC University Cosine similarities Matrix factorization ALS Recommendation system Cataloged from PDF version of thesis report. Includes bibliographical references (pages 60-61). This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. The 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. Ashique Mohaimin Ornab Sakia Chowdhury Seevieta Biswas Toa B. Computer Science and Engineering 2018-02-20T09:17:10Z 2018-02-20T09:17:10Z 2017 12/26/2017 Thesis ID 13201080 ID 14101252 ID 14101003 http://hdl.handle.net/10361/9535 en BRAC 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. 61 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Cosine similarities Matrix factorization ALS Recommendation system
spellingShingle	Cosine similarities Matrix factorization ALS Recommendation system Ornab, Ashique Mohaimin Chowdhury, Sakia Toa, Seevieta Biswas An empirical study of collaborative filtering algorithms for building a diet recommendation system
description	Cataloged from PDF version of thesis report.
author2	Arif, Hossain
author_facet	Arif, Hossain Ornab, Ashique Mohaimin Chowdhury, Sakia Toa, Seevieta Biswas
format	Thesis
author	Ornab, Ashique Mohaimin Chowdhury, Sakia Toa, Seevieta Biswas
author_sort	Ornab, Ashique Mohaimin
title	An empirical study of collaborative filtering algorithms for building a diet recommendation system
title_short	An empirical study of collaborative filtering algorithms for building a diet recommendation system
title_full	An empirical study of collaborative filtering algorithms for building a diet recommendation system
title_fullStr	An empirical study of collaborative filtering algorithms for building a diet recommendation system
title_full_unstemmed	An empirical study of collaborative filtering algorithms for building a diet recommendation system
title_sort	empirical study of collaborative filtering algorithms for building a diet recommendation system
publisher	BRAC University
publishDate	2018
url	http://hdl.handle.net/10361/9535
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An empirical study of collaborative filtering algorithms for building a diet recommendation system

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