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Similarity Measures and Models for Movie Series Recommender System

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Internet Science (INSCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11193))

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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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Notes

  1. 1.

    We also experimented with other tools for training word embeddings like FastText and tried larged word embedding models provided by RusVectores and Russian Distributional Thesaurus, but in both cases the results appeared to be worse than those achieved with word2vec trained on the movie series scripts. These results are not reported in this paper due to space limits.

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Author information

Authors and Affiliations

  1. St.-Petersburg State University, Saint-Petersburg, Russian Federation

    Bliznuk Danil, Yagunova Elena & Pronoza Ekaterina

Authors
  1. Bliznuk Danil
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  2. Yagunova Elena
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  3. Pronoza Ekaterina
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Corresponding author

Correspondence to Pronoza Ekaterina .

Editor information

Editors and Affiliations

  1. School of Journalism and Mass Communications, Saint Petersburg State University, St. Petersburg, Russia

    Svetlana S. Bodrunova

A Appendix

A Appendix

See Table 3.

Table 3. Genres of movie series from Kinopoisk
Full size table

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Danil, B., Elena, Y., Ekaterina, P. (2018). Similarity Measures and Models for Movie Series Recommender System. In: Bodrunova, S. (eds) Internet Science. INSCI 2018. Lecture Notes in Computer Science(), vol 11193. Springer, Cham. https://doi.org/10.1007/978-3-030-01437-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-01437-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01436-0

  • Online ISBN: 978-3-030-01437-7

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