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User profiling in text-based recommender systems based on distributed word representations. / Alekseev, Anton; Nikolenko, Sergey.

Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. ред. / Natalia Loukachevitch; Alexander Panchenko; Konstantin Vorontsov; Valeri G. Labunets; Andrey V. Savchenko; Dmitry I. Ignatov; Sergey I. Nikolenko; Mikhail Yu. Khachay. Springer Nature, 2017. стр. 196-207 (Communications in Computer and Information Science; Том 661).

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Harvard

Alekseev, A & Nikolenko, S 2017, User profiling in text-based recommender systems based on distributed word representations. в N Loukachevitch, A Panchenko, K Vorontsov, VG Labunets, AV Savchenko, DI Ignatov, SI Nikolenko & MY Khachay (ред.), Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Communications in Computer and Information Science, Том. 661, Springer Nature, стр. 196-207, 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, Yekaterinburg, Российская Федерация, 7/04/16. https://doi.org/10.1007/978-3-319-52920-2_19

APA

Alekseev, A., & Nikolenko, S. (2017). User profiling in text-based recommender systems based on distributed word representations. в N. Loukachevitch, A. Panchenko, K. Vorontsov, V. G. Labunets, A. V. Savchenko, D. I. Ignatov, S. I. Nikolenko, & M. Y. Khachay (Ред.), Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers (стр. 196-207). (Communications in Computer and Information Science; Том 661). Springer Nature. https://doi.org/10.1007/978-3-319-52920-2_19

Vancouver

Alekseev A, Nikolenko S. User profiling in text-based recommender systems based on distributed word representations. в Loukachevitch N, Panchenko A, Vorontsov K, Labunets VG, Savchenko AV, Ignatov DI, Nikolenko SI, Khachay MY, Редакторы, Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Springer Nature. 2017. стр. 196-207. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-52920-2_19

Author

Alekseev, Anton ; Nikolenko, Sergey. / User profiling in text-based recommender systems based on distributed word representations. Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Редактор / Natalia Loukachevitch ; Alexander Panchenko ; Konstantin Vorontsov ; Valeri G. Labunets ; Andrey V. Savchenko ; Dmitry I. Ignatov ; Sergey I. Nikolenko ; Mikhail Yu. Khachay. Springer Nature, 2017. стр. 196-207 (Communications in Computer and Information Science).

BibTeX

@inproceedings{0d75c0216c8241d68d266210b640ef2c,
title = "User profiling in text-based recommender systems based on distributed word representations",
abstract = "We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user{\textquoteright}s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.",
keywords = "Distributed word representations, Recommender systems, User profiling, recommender system, user profiling, natural language processing, machine learning, vector semantics",
author = "Anton Alekseev and Sergey Nikolenko",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016 ; Conference date: 07-04-2016 Through 09-04-2016",
year = "2017",
doi = "10.1007/978-3-319-52920-2_19",
language = "English",
isbn = "9783319529196",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "196--207",
editor = "Natalia Loukachevitch and Alexander Panchenko and Konstantin Vorontsov and Labunets, {Valeri G.} and Savchenko, {Andrey V.} and Ignatov, {Dmitry I.} and Nikolenko, {Sergey I.} and Khachay, {Mikhail Yu.}",
booktitle = "Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers",
address = "Germany",

}

RIS

TY - GEN

T1 - User profiling in text-based recommender systems based on distributed word representations

AU - Alekseev, Anton

AU - Nikolenko, Sergey

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user’s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.

AB - We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user’s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.

KW - Distributed word representations

KW - Recommender systems

KW - User profiling

KW - recommender system

KW - user profiling

KW - natural language processing

KW - machine learning

KW - vector semantics

UR - http://www.scopus.com/inward/record.url?scp=85014188521&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-52920-2_19

DO - 10.1007/978-3-319-52920-2_19

M3 - Conference contribution

AN - SCOPUS:85014188521

SN - 9783319529196

T3 - Communications in Computer and Information Science

SP - 196

EP - 207

BT - Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers

A2 - Loukachevitch, Natalia

A2 - Panchenko, Alexander

A2 - Vorontsov, Konstantin

A2 - Labunets, Valeri G.

A2 - Savchenko, Andrey V.

A2 - Ignatov, Dmitry I.

A2 - Nikolenko, Sergey I.

A2 - Khachay, Mikhail Yu.

PB - Springer Nature

T2 - 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016

Y2 - 7 April 2016 through 9 April 2016

ER -

ID: 95167506