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undergraduate thesis
Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:055137
Šobot, Ana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Šobot, A. (2020). Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:055137

Šobot, Ana. "Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:055137

Šobot, Ana. "Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:055137

Šobot, A. (2020). 'Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:055137

Šobot A. Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:055137

A. Šobot, "Preporučivanje proizvoda na temelju sadržaja koji generiraju korisnici", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:055137

Metadata
TitlePreporučivanje proizvoda na temelju sadržaja koji generiraju korisnici
Title (english)Product Recommendation based on User-generated Content
AuthorAna Šobot
MentorMarina Bagić Babac (mentor)
Committee memberMarina Bagić Babac (predsjednik povjerenstva)
Committee memberNikica Hlupić (član povjerenstva)
Committee memberKrešimir Fertalj (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-08-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj završni rad ima za cilj unaprijediti mobilnu aplikaciju za praćenje stanja kože funkcionalnošću preporučivanja proizvoda korisnicima. Konkretno, na temelju online recenzija korisnika o odabranim proizvodima za kožu, pomoću analiza sentimenta utvrdit će se koliko su korisnici zadovoljni tim proizvodima. Pored toga, upotrijebit će se algoritam strojnog učenja za predviđanje zadovoljstva korisnika. Korištenjem ove metodologije, krajnji je cilj izgraditi algoritam preporuke koji se temelji na korisničkim ocjenama proizvoda, opisu proizvoda te osobnim podacima korisnika poput preferencija i rezultata skeniranja lica.
Abstract (english)
This thesis aims to upgrade the mobile application for skin monitoring with the functionality of product recommendations to users. Specifically, based on online user reviews for selected skin products, sentiment analysis will be used to determine how much the users are satisfied with these products. In addition, a machine learning algorithm will be used to predict user satisfaction. Using this methodology, the final aim is to build a recommendation algorithm that is based on inferred product ratings, product description itself, and user personal data such as user preferences and face scan results.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:055137
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 10:04:32