Unit:
Department of Informatics and TelecommunicationsΠληροφορική
Author:
ROUSSAKI AIKATERINH
Supervisors info:
ΙΖΑΜΠΩ ΚΑΡΑΛΗ
ΕΠΙΚΟΥΡΗ ΚΑΘΗΓΗΤΡΙΑ
ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ
ΕΘΝΙΚΟ ΚΑΙ ΚΑΠΟΔΙΣΤΡΙΑΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ
Original Title:
Music Recommendation Systems using Dempster Shafer Theory
Translated title:
Music Recommendation Systems using Dempster Shafer Theory
Summary:
In this thesis, we deal with the subject of Handling Uncertainty by using Dempster-Shafer
Theory of Evidence. The purpose of this project is to use the subject of handling uncertainty
as a recommendation technique in a Music Recommendation System (MRS). We
use the Dempster’s rule of combination and more specifically a Monte Carlo algorithm
which is an approximation algorithm to compute the rule. Both the Music Recommendation
model and the Dempster’s rule of combination are implemented with the use of the
programming language Python and it’s libraries.
Main subject category:
Technology - Computer science
Keywords:
uncertainty, dempster-shafer, approximation algorithms,montecarlo, recommendation systems, music recommendation, Python, music-metadata
