Movie Recommendation System based on Dempster-Shafer theory

Graduate Thesis uoadl:2946844 7 Read counter

Unit:
Department of Informatics and Telecommunications
Πληροφορική
Deposit date:
2021-06-05
Year:
2021
Author:
Papasotiriou Ilias
Supervisors info:
Ιζαμπώ Καράλη, Επίκουρη Καθηγήτρια, Τμήμα Πληροφορικής και Τηλεπικοινωνιών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Original Title:
Movie Recommendation System based on Dempster-Shafer theory
Languages:
English
Greek
Translated title:
Movie Recommendation System based on Dempster-Shafer theory
Summary:
In this thesis, we study the subject of Handling Uncertainty in Recommendation Systems. We implemented a Collaborative Filtering movie recommendation system using the Python programming language. We use Dempster-Shafer theory to propagate the uncertainties arising from imperfections in user ratings to the decision-making process. By converting ratings into mass functions and pignistic probabilities, we measure the similarities between users and use the Dempster’s rule of combination to predict user ratings in movies they have not rated.
Main subject category:
Technology - Computer science
Keywords:
Dempster-Shafer theory, Uncertainty, Recommendation system, Python
Index:
Yes
Number of index pages:
4
Contains images:
Yes
Number of references:
25
Number of pages:
51
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