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Title:
Gradnja priporočilnega sistema na platformi Turi
Authors:
ID
Klezin, Florijan
(Author)
ID
Podgorelec, Vili
(Mentor)
More about this mentor...
Files:
UN_Klezin_Florijan_2017.pdf
(2,35 MB)
MD5: CE0D3C2E398DC6B34E911FE7B1423881
PID:
20.500.12556/dkum/dc218fac-ab1f-432a-ade2-ea023134102c
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
Veliki spletni trgovci ponujajo na milijone izdelkov. Pregledno iskanje po njih strankam predstavlja velik izziv. Priporočilni sistemi so postali ena izmed rešitev tega problema, saj tak sistem strankam predlaga izdelke, za katere obstaja večja verjetnost, da jih bodo zanimali. V tej diplomski nalogi smo opisali in predstavili priporočilne sisteme, opisali najbolj uporabljane algoritmične pristope k priporočanju izdelkov, vzorce razvoja kompleksnejših priporočilnih sistemov in načine, kako ovrednotimo priporočilni sistem. Najbolj smo se osredotočili na gradnjo priporočilnega sistema s platformo za strojno učenje Turi.
Keywords:
priporočilni sistemi
,
Turi
,
Graphlab
,
strojno učenje
Place of publishing:
[Maribor
Publisher:
F. Klezin
Year of publishing:
2017
PID:
20.500.12556/DKUM-67395
UDC:
004.832(043.2)
COBISS.SI-ID:
20900630
NUK URN:
URN:SI:UM:DK:S5ZHSTQR
Publication date in DKUM:
17.10.2017
Views:
9180
Downloads:
119
Metadata:
Categories:
KTFMB - FERI
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:
KLEZIN, Florijan, 2017,
Gradnja priporočilnega sistema na platformi Turi
[online]. Bachelor’s thesis. Maribor : F. Klezin. [Accessed 21 January 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=67395
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Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
16.08.2017
Secondary language
Language:
English
Title:
The development of a recommendation system on Turi platform
Abstract:
Large online merchants offer millions of products. A transparent search products is becoming a great challenge for customers. Recommendation systems have become one of the solutions to this problem, since such a system offers products that are more likely to be interesting for the user. In this Thesis we have described and presented recommendation systems, described the most used algorithmic approaches to product recommendations, patterns of development of more complex recommendation systems, and evaluation of the recommendation systems. Most of all, we have focused on building a recommendation system with the Turi machine learning platform.
Keywords:
recommendation systems
,
Turi
,
Graphlab
,
machine learning
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