Standard
Similarity Measures and Models for Movie Series Recommender System. / Близнюк, Данил Дмитриевич; Ягунова, Елена Викторовна; Проноза, Екатерина Валерьевна.
Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings. Vol. 11193 LNCS Springer Nature, 2018. p. 181-193 (Lecture Notes in Computer Science; Vol. 11193).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Близнюк, ДД, Ягунова, ЕВ & Проноза, ЕВ 2018,
Similarity Measures and Models for Movie Series Recommender System. in
Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings. vol. 11193 LNCS, Lecture Notes in Computer Science, vol. 11193, Springer Nature, pp. 181-193.
https://doi.org/10.1007/978-3-030-01437-7_15
APA
Близнюк, Д. Д., Ягунова, Е. В., & Проноза, Е. В. (2018).
Similarity Measures and Models for Movie Series Recommender System. In
Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings (Vol. 11193 LNCS, pp. 181-193). (Lecture Notes in Computer Science; Vol. 11193). Springer Nature.
https://doi.org/10.1007/978-3-030-01437-7_15
Vancouver
Author
Близнюк, Данил Дмитриевич ; Ягунова, Елена Викторовна ; Проноза, Екатерина Валерьевна. /
Similarity Measures and Models for Movie Series Recommender System. Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings. Vol. 11193 LNCS Springer Nature, 2018. pp. 181-193 (Lecture Notes in Computer Science).
BibTeX
@inproceedings{0b3657c41d39453aafbf8e29fe7f886a,
title = "Similarity Measures and Models for Movie Series Recommender System",
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).",
keywords = "Recommender system, Clustering algorithm, Movie series scripts, Vector space model, Semantic similarity measure",
author = "Близнюк, {Данил Дмитриевич} and Ягунова, {Елена Викторовна} and Проноза, {Екатерина Валерьевна}",
note = "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",
year = "2018",
month = sep,
day = "25",
doi = "10.1007/978-3-030-01437-7_15",
language = "English",
isbn = "978-3-030-01436-0",
volume = "11193 LNCS",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "181--193",
booktitle = "Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Similarity Measures and Models for Movie Series Recommender System
AU - Близнюк, Данил Дмитриевич
AU - Ягунова, Елена Викторовна
AU - Проноза, Екатерина Валерьевна
N1 - 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
PY - 2018/9/25
Y1 - 2018/9/25
N2 - 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).
AB - 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).
KW - Recommender system
KW - Clustering algorithm
KW - Movie series scripts
KW - Vector space model
KW - Semantic similarity measure
U2 - 10.1007/978-3-030-01437-7_15
DO - 10.1007/978-3-030-01437-7_15
M3 - Conference contribution
SN - 978-3-030-01436-0
VL - 11193 LNCS
T3 - Lecture Notes in Computer Science
SP - 181
EP - 193
BT - Internet Science - 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24-26, 2018, Proceedings
PB - Springer Nature
ER -
