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Musical recommendations and personalization in a social network. / Bugaychenko, Dmitry; Dzuba, Alexandr.

Proceedings of the 7th ACM Conference on Recommender Systems. Association for Computing Machinery, 2013. p. 367-370.

Research output: Chapter in Book/Report/Conference proceeding › Article in an anthology

Harvard

Bugaychenko, D & Dzuba, A 2013, Musical recommendations and personalization in a social network. in Proceedings of the 7th ACM Conference on Recommender Systems. Association for Computing Machinery, pp. 367-370. https://doi.org/10.1145/2507157.2507192

APA

Bugaychenko, D., & Dzuba, A. (2013). Musical recommendations and personalization in a social network. In Proceedings of the 7th ACM Conference on Recommender Systems (pp. 367-370). Association for Computing Machinery. https://doi.org/10.1145/2507157.2507192

Vancouver

Bugaychenko D, Dzuba A. Musical recommendations and personalization in a social network. In Proceedings of the 7th ACM Conference on Recommender Systems. Association for Computing Machinery. 2013. p. 367-370 https://doi.org/10.1145/2507157.2507192

Author

Bugaychenko, Dmitry ; Dzuba, Alexandr. / Musical recommendations and personalization in a social network. Proceedings of the 7th ACM Conference on Recommender Systems. Association for Computing Machinery, 2013. pp. 367-370

BibTeX

@inbook{ea18b3ce820f485683aa42a0c91bd6e0,
title = "Musical recommendations and personalization in a social network",
abstract = "This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like {"}recommend a sequence{"} and {"}find similar items{"} the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes in the user's behavior and needs in the real time. The core component of the algorithms is a taste graph containing information about different entities (users, tracks, artists, etc.) and relations between them (for example, user A likes song B with certainty X, track B created by artist C, artist C is similar to artist D with certainty Y and so on). Using the graph it is possible to select tracks a user would most prob",
keywords = "recommender systems",
author = "Dmitry Bugaychenko and Alexandr Dzuba",
year = "2013",
doi = "10.1145/2507157.2507192",
language = "English",
isbn = "978-1-4503-2409-0",
pages = "367--370",
booktitle = "Proceedings of the 7th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery",
address = "United States",

}

RIS

TY - CHAP

T1 - Musical recommendations and personalization in a social network

AU - Bugaychenko, Dmitry

AU - Dzuba, Alexandr

PY - 2013

Y1 - 2013

N2 - This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes in the user's behavior and needs in the real time. The core component of the algorithms is a taste graph containing information about different entities (users, tracks, artists, etc.) and relations between them (for example, user A likes song B with certainty X, track B created by artist C, artist C is similar to artist D with certainty Y and so on). Using the graph it is possible to select tracks a user would most prob

AB - This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes in the user's behavior and needs in the real time. The core component of the algorithms is a taste graph containing information about different entities (users, tracks, artists, etc.) and relations between them (for example, user A likes song B with certainty X, track B created by artist C, artist C is similar to artist D with certainty Y and so on). Using the graph it is possible to select tracks a user would most prob

KW - recommender systems

U2 - 10.1145/2507157.2507192

DO - 10.1145/2507157.2507192

M3 - Article in an anthology

SN - 978-1-4503-2409-0

SP - 367

EP - 370

BT - Proceedings of the 7th ACM Conference on Recommender Systems

PB - Association for Computing Machinery

ER -

ID: 4656145