%0 Conference Proceedings
%T Movie Recommendation System Based on Character Graph Embeddings
%+ Department of Computer Engineering and Informatics [Patras]
%A Kounelis, Agisilaos
%A Vikatos, Pantelis
%A Makris, Christos
%Z Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Crete, Greece
%Y Ilias Maglogiannis
%Y John Macintyre
%Y Lazaros Iliadis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops
%V AICT-628
%P 418-430
%8 2021-06-25
%D 2021
%R 10.1007/978-3-030-79157-5_34
%K Recommendation systems
%K Character graphs
%K Graph embeddings
%Z Computer Science [cs]Conference papers
%X This paper presents a novel approach for recommending movies based on weighted Character Graphs. This approach proposes a dedicated crawler that gathers movie screenplays and a methodology of character graphs generation that contains all the necessary information needed for the representation of movie plots. A representative vector is extracted for each graph and used along with user ratings, as an input for a gradient boosting algorithm to predict movie ratings. The proposed method is tested on a publicly available MovieLens dataset and it was experimentally shown that it outperforms the fundamental collaborative filtering recommendation algorithms.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-03789011/document
%2 https://inria.hal.science/hal-03789011/file/517424_1_En_34_Chapter.pdf
%L hal-03789011
%U https://inria.hal.science/hal-03789011
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-628