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undergraduate thesis
Sustav preporuke događanja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:270468
Hlup, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Hlup, F. (2024). Sustav preporuke događanja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:270468

Hlup, Filip. "Sustav preporuke događanja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:270468

Hlup, Filip. "Sustav preporuke događanja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:270468

Hlup, F. (2024). 'Sustav preporuke događanja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:270468

Hlup F. Sustav preporuke događanja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:270468

F. Hlup, "Sustav preporuke događanja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:270468

Metadata
TitleSustav preporuke događanja
Title (english)Event recommendation system
AuthorFilip Hlup
MentorKrešimir Fertalj (mentor)
Committee memberKrešimir Fertalj (predsjednik povjerenstva)
Committee memberJuraj Dončević (član povjerenstva)
Committee memberAndrej Slapničar (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog završnog rada je istražiti kako integracija sustava za preporuke i velikih jezičnih modela (LLM-ova), poput GPT-4, može unaprijediti korisničko iskustvo pružajući personalizirane preporuke. Predstavljen je sustav koji prikuplja osnovne informacije i interese korisnika te uz pomoć GPT-4 modela stvara personalizirani profil korisnika, koji se kasnije nadograđuje povratnim informacijama. Rezultati pokazuju da integracija LLM-ova značajno poboljšava kvalitetu preporuka, pružajući korisnicima personalizirano iskustvo i veću vrijednost tvrtkama.
Abstract (english)
The aim of this thesis is to explore how integrating recommendation systems and large language models (LLMs), such as GPT-4, can enhance user experience by providing personalized recommendations. The system collects basic information and user interests, creating a personalized profile with the GPT-4 model, which is updated with feedback. Results show that LLM integration significantly improves recommendation quality, providing a personalized user experience and greater value to companies.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:270468
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 on2024-10-12 20:47:53