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undergraduate thesis
Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:590465
Raguž, Andrija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Raguž, A. (2020). Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:590465

Raguž, Andrija. "Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:590465

Raguž, Andrija. "Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:590465

Raguž, A. (2020). 'Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 March 2025, https://urn.nsk.hr/urn:nbn:hr:168:590465

Raguž A. Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2025 March 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:590465

A. Raguž, "Ostvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:590465

Metadata
TitleOstvarenje sustava preporučivanja koristeći algoritam suradničkog filtriranja
Title (english)Recommendation Systems Implementation Using Collaborative Filtering Algorithm
AuthorAndrija Raguž
MentorKlemo Vladimir (mentor)
Committee memberKlemo Vladimir (predsjednik povjerenstva)
Committee memberAdrian Satja Kurdija (član povjerenstva)
Committee memberPetar Afrić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu objašnjeni su sustavi preporučivanja. Sustavi preporučivanja služe online uslugama za bolju i personaliziraniju ponudu. Dijele se na tri osnovne skupine: sustave temeljene na sadržaju, sustave temeljene na suradničkom filtriranju i hibridne sustave koji su mješavina prethodna dva. Ovdje je pobliže opisan i implementiran algoritam suradničkog filtriranja. Sustavi suradničkog filtriranja dijele se na one temeljene na korisnik – korisnik odnosu i one temeljene na predmet – predmet... More odnosu. Suradničko filtriranje djeluje tako što izračuna sličnost između korisnika ili predmeta u bazi podataka pomoću neke matematičke funkcije npr. kosinus funkcija, Jaccardova funkcija itd. i na osnovu te sličnosti nađe određeni broj najsličnijih korisnika ili predmeta. Zatim koristeći njihove ocjene generira ocjene predmeta koje aktivni korisnik nije ocijenio i na osnovu toga stvara preporuke. Jednostavni oblik suradničkog filtriranja je implementiran koristeći programski jezik Python. Također, implementirana je mjera za evaluaciju sustava preporučivanja RMSE. RMSE uspoređuje stvarne i procijenjene vrijednosti ocjena i na osnovu toga vrednuje sustav preporučivanja. Less
Abstract (english)
This paper explains recommendation systems. Recommendation systems are used by online services to provide a better and more personalized offer. They are divided into three basic groups: content-based systems, collaborative filtering-based systems, and hybrid systems that are a mixture of the previous two. The collaborative filtering algorithm is described and implemented in more detail here. Collaborative filtering systems are divided into those based on user-user relationship and those... More based on item-item relationship. Collaborative filtering works by calculating a similarity between a user or an item in a database using a mathematical function such as a cosine function, a Jaccard function, etc., and based on that similarity finds a number of the most similar users or items. It then uses their grades to generate item grades that the active user has not graded and creates recommendations based on that. A simple form of collaborative filtering is implemented using the Python programming language. Also, a measure was implemented to evaluate the RMSE recommendation system. RMSE compares actual and estimated scores and evaluates the recommendation system accordingly. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:590465
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 on2023-01-13 16:00:27
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