Abstract
In this paper we propose a method of movie series recommender system development. Our recommender system is content-based, and movie series are represented by their scripts. We experiment with several semantic similarity measures, lexico-morphological metrics, keywords and vector space models to extract similar movie series. Evaluation is conducted in the experiment with informants. The best results are achieved by distributional semantic approach (i.e., using word2vec technology).
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CITATION STYLE
Danil, B., Elena, Y., & Ekaterina, P. (2018). Similarity measures and models for movie series recommender system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11193 LNCS, pp. 181–193). Springer Verlag. https://doi.org/10.1007/978-3-030-01437-7_15
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