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Title:Implementacija priporočilnega sistema na osnovi grafa, izdelanega iz n-teric
Authors:ID Mažgon, Lovro (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf MAG_Mazgon_Lovro_2016.pdf (3,55 MB)
MD5: AE2F04180CEC1893003409E9177F03E0
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je opisano področje priporočilnih sistemov. Zapisali smo njihovo formalno definicijo, naloge, ki jih opravljajo, ter vire podatkov in znanja. Navedli smo različne tehnike podajanja priporočil ter opisali pristope k uspešni evalvaciji priporočilnih sistemov. V praktičnem delu smo izdelali priporočilni sistem za priporočanje programskih ogrodij, namenjen programerjem. Implementirali smo tri priporočilne algoritme, ki bazirajo na podatkih v obliki grafa, in jih primerjali z algoritmom na osnovi asociacijskih pravil. Algoritme smo optimizirali in testirali na podatkih, pridobljenih s spletnega portala Stack Overflow. Pridobljeni rezultati nakazujejo, da ima uporabljen pristop visok potencial ter da je v pravih razmerah smiseln in uporaben.
Keywords:priporočilni sistem, graf, naključni sprehod, asociacijska pravila
Place of publishing:[Maribor
Publisher:L. Mažgon
Year of publishing:2016
PID:20.500.12556/DKUM-59775 New window
UDC:004.4'275:004.021(043.2)
COBISS.SI-ID:19796758 New window
NUK URN:URN:SI:UM:DK:ORSNWQOR
Publication date in DKUM:04.07.2016
Views:1398
Downloads:222
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Implementation of a Recommender System Based on a Graph Extracted from Itemsets
Abstract:The thesis addresses the area of recommender systems. We described their formal definition, tasks, data and knowledge sources. Furthermore, we defined various recommender techniques as well as approaches to the successful evaluation of recommender systems. Our empirical work covers the construction of a recommender system for programmers, which recommends frameworks. We implemented three recommender algorithms, which are based on graph data, as well as one based on association rules. We optimized the algorithms and tested them on data obtained from the website Stack Overflow. The results suggest that the approach has high potential and that it is reasonable and useful in the right circumstances.
Keywords:recommender system, graph, random walk, association rules


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