%0 Conference Proceedings
%T Improving Movie Recommendation Systems Filtering by Exploiting User-Based Reviews and Movie Synopses
%+ Department of Computer Engineering and Informatics [Patras]
%A Iliopoulou, Konstantina
%A Kanavos, Andreas
%A Ilias, Aristidis
%A Makris, Christos
%A Vonitsanos, Gerasimos
%Z Part 2: 9th Mining Humanistic Data Workshop
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Neos Marmaras, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops
%V AICT-585
%P 187-199
%8 2020-06-05
%D 2020
%R 10.1007/978-3-030-49190-1_17
%K Recommendation systems
%K Movie Recommendation Systems
%K Collaborative Filtering
%K Content-based Filtering
%K Text analysis
%Z Computer Science [cs]Conference papers
%X This paper addresses the subject of Movie Recommendation Systems, focusing on two of the most well-known filtering techniques, Collaborative Filtering and Content-based Filtering. The first approach proposes a supervised probabilistic Bayesian model that forms recommendations based on the previous evaluations of other movies the user has watched. The second approach composes an unsupervised learning technique that forms clusters of users, using the K-Means algorithm, based on their preference of different movie genres, as it is expressed through their ratings. Both of the above approaches are compared to each other as well as to a basic method known as Weighted Sum, which makes predictions based on the cosine similarity and the euclidean distance between users and movies. In addition, Content-based Filtering is implemented through K-Means clustering techniques that focus on identifying the resemblance between movie plots. The first approach clusters movies according to the Tf/Idf weighting scheme, applying weights to the terms of movie plots. The latter identifies the likeness between movie plots, utilizing the BM25 algorithm. The efficiency of the above methods is calculated through the Accuracy metric.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-03677624/document
%2 https://inria.hal.science/hal-03677624/file/500088_1_En_17_Chapter.pdf
%L hal-03677624
%U https://inria.hal.science/hal-03677624
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-585