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Title:Spletni katalog za ocenjevanje in priporočanje knjig
Authors:ID Tot, Vanesa (Author)
ID Lukač, Niko (Mentor) More about this mentor... New window
ID Jeromel, Aljaž (Comentor)
Files:.pdf VS_Tot_Vanesa_2024.pdf (2,70 MB)
MD5: B3DA9E717FFFE3E480DE54635D157DDC
 
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 predstavimo razvoj spletnega kataloga za ocenjevanje in priporočanje knjig, ki uporablja kolaborativno filtriranje za personalizirano uporabniško izkušnjo. Spoznamo sorodne rešitve na trgu, ki uporabljajo priporočilne sisteme. Obravnavamo glavne pristope priporočilnih sistemov, kot sta kolaborativno in vsebinsko filtriranje in se pri razvoju aplikacije osredotočimo na kolaborativno filtriranje. Na koncu izvedemo analizo in testiramo delovanje izbranega algoritma na množici uporabnikov, kjer preverimo, kako učinkovito algoritem priporoča knjige na podlagi sosednjih uporabnikov.
Keywords:priporočilni sistem, spletna aplikacija, kolaborativno filtriranje, vsebinsko filtriranje
Place of publishing:Maribor
Year of publishing:2024
PID:20.500.12556/DKUM-90795 New window
Publication date in DKUM:22.10.2024
Views:0
Downloads:32
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.09.2024

Secondary language

Language:English
Title:Web catalog for rating and recommending books
Abstract:In this thesis, we present the development of an online catalogue for book rating and recommending that uses collaborative filtering for a personalised user experience. We learn about related solutions on the market that use recommendation systems. We discuss the main approaches of recommender systems, such as collaborative and content-based filtering, and focus on collaborative filtering in the development of the application. Finally, we perform an analysis and test the performance of the algorithm on a set of users, where we check how efficiently the algorithm recommends books based on neighbouring users.
Keywords:recommendation system, web application, collaborative filtering, content filtering


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