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2018 | OriginalPaper | Buchkapitel

Similarity Measures and Models for Movie Series Recommender System

verfasst von : Bliznuk Danil, Yagunova Elena, Pronoza Ekaterina

Erschienen in: Internet Science

Verlag: Springer International Publishing

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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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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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Titel: Similarity Measures and Models for Movie Series Recommender System
verfasst von: Bliznuk Danil
Yagunova Elena
Pronoza Ekaterina
Verlag: Springer International Publishing
Buch: Internet Science
Print ISBN: 978-3-030-01436-0

Electronic ISBN: 978-3-030-01437-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01437-7_15

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