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Communication Dans Un Congrès Année : 2021

Movie Recommendation System Based on Character Graph Embeddings

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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.
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hal-03789011 , version 1 (27-09-2022)

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Agisilaos Kounelis, Pantelis Vikatos, Christos Makris. Movie Recommendation System Based on Character Graph Embeddings. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.418-430, ⟨10.1007/978-3-030-79157-5_34⟩. ⟨hal-03789011⟩
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