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Title:Priporočanje vsebin z aproksimativnim kolaborativnim filtriranjem : diplomsko delo
Authors:ID Pincin, Eva (Author)
ID Lukač, Niko (Mentor) More about this mentor... New window
ID Jesenko, David (Comentor)
Files:.pdf UN_Pincin_Eva_2026.pdf (1,70 MB)
MD5: A0E964535AE854B6EE9499A7E3DB586E
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu predstavljamo tri algoritme za kolaborativno filtriranje: algoritem najbližjih sosedov z uporabo k-d drevesa, uteženi Slope One ter model z nevronsko mrežo. Za vse implementiramo tudi aproksimativno različico, s katero zmanjšamo časovno in pomnilniško zahtevnost. Delovanje algoritmov analiziramo na podatkovni množici, pri čemer primerjamo njihovo natančnost, čas izvajanja in prostorsko zahtevnost. Cilj diplomskega dela je ugotoviti vpliv aproksimativnih metod na kakovost priporočil ter določiti najprimernejši pristop za uporabo v sistemih z veliko količino podatkov.
Keywords:priporočilni sistem, aproksimacija, KNN, Slope One, nevronska mreža
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[E. Pincin]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (VII, 43 str.))
PID:20.500.12556/DKUM-97991 New window
UDC:004.8.021(043.2)
COBISS.SI-ID:280908803 New window
Publication date in DKUM:29.05.2026
Views:215
Downloads:27
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
Link:http://creativecommons.org/licenses/by-nc/4.0/
Description:A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.
Licensing start date:06.05.2026

Secondary language

Language:English
Title:Content Recommendation Using Approximate Collaborative Filtering
Abstract:In this diploma thesis, we present three collaborative filtering algorithms: the k-nearest neighbours algorithm using a k-d tree, the weighted Slope One method, and a neural network model. For each approach, we also implement an approximate version in order to reduce computational time and memory requirements. The performance of the algorithms is analysed on a dataset, where their accuracy, execution time, and space complexity are compared. The aim of this diploma thesis is to evaluate the impact of approximate methods on the quality of recommendations and to determine the most suitable approach for use in systems with large amounts of data.
Keywords:recommender system, approximation, KNN, Slope One, neural network


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