Standard
AspeRa : Aspect-based rating prediction model. / Nikolenko, Sergey I.; Tutubalina, Elena; Malykh, Valentin; Shenbin, Ilya; Alekseev, Anton.
Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. ред. / Philipp Mayr; Claudia Hauff; Benno Stein; Norbert Fuhr; Leif Azzopardi; Djoerd Hiemstra. Springer Nature, 2019. стр. 163-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11438 LNCS).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Nikolenko, SI, Tutubalina, E, Malykh, V, Shenbin, I
& Alekseev, A 2019,
AspeRa: Aspect-based rating prediction model. в P Mayr, C Hauff, B Stein, N Fuhr, L Azzopardi & D Hiemstra (ред.),
Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 11438 LNCS, Springer Nature, стр. 163-171, 41st European Conference on Information Retrieval, ECIR 2019, Cologne, Германия,
14/04/19.
https://doi.org/10.1007/978-3-030-15719-7_21
APA
Nikolenko, S. I., Tutubalina, E., Malykh, V., Shenbin, I.
, & Alekseev, A. (2019).
AspeRa: Aspect-based rating prediction model. в P. Mayr, C. Hauff, B. Stein, N. Fuhr, L. Azzopardi, & D. Hiemstra (Ред.),
Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings (стр. 163-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11438 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-15719-7_21
Vancouver
Nikolenko SI, Tutubalina E, Malykh V, Shenbin I
, Alekseev A.
AspeRa: Aspect-based rating prediction model. в Mayr P, Hauff C, Stein B, Fuhr N, Azzopardi L, Hiemstra D, Редакторы, Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. Springer Nature. 2019. стр. 163-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-15719-7_21
Author
Nikolenko, Sergey I. ; Tutubalina, Elena ; Malykh, Valentin ; Shenbin, Ilya
; Alekseev, Anton. /
AspeRa : Aspect-based rating prediction model. Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. Редактор / Philipp Mayr ; Claudia Hauff ; Benno Stein ; Norbert Fuhr ; Leif Azzopardi ; Djoerd Hiemstra. Springer Nature, 2019. стр. 163-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{9687607682e14f55832ac27a54672686,
title = "AspeRa: Aspect-based rating prediction model",
abstract = "We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.",
keywords = "Aspect-based recommendation, Aspect-based sentiment analysis, Deep learning, Explainable recommendation, Neural network, Recommender systems, User reviews, information retrieval, machine learning, deep learning, aspect extraction, recommender systems, rating prediction",
author = "Nikolenko, {Sergey I.} and Elena Tutubalina and Valentin Malykh and Ilya Shenbin and Anton Alekseev",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 41st European Conference on Information Retrieval, ECIR 2019 ; Conference date: 14-04-2019 Through 18-04-2019",
year = "2019",
doi = "10.1007/978-3-030-15719-7_21",
language = "English",
isbn = "9783030157180",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "163--171",
editor = "Philipp Mayr and Claudia Hauff and Benno Stein and Norbert Fuhr and Leif Azzopardi and Djoerd Hiemstra",
booktitle = "Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - AspeRa
T2 - 41st European Conference on Information Retrieval, ECIR 2019
AU - Nikolenko, Sergey I.
AU - Tutubalina, Elena
AU - Malykh, Valentin
AU - Shenbin, Ilya
AU - Alekseev, Anton
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.
AB - We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.
KW - Aspect-based recommendation
KW - Aspect-based sentiment analysis
KW - Deep learning
KW - Explainable recommendation
KW - Neural network
KW - Recommender systems
KW - User reviews
KW - information retrieval
KW - machine learning
KW - deep learning
KW - aspect extraction
KW - recommender systems
KW - rating prediction
UR - http://www.scopus.com/inward/record.url?scp=85064882490&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15719-7_21
DO - 10.1007/978-3-030-15719-7_21
M3 - Conference contribution
AN - SCOPUS:85064882490
SN - 9783030157180
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 171
BT - Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings
A2 - Mayr, Philipp
A2 - Hauff, Claudia
A2 - Stein, Benno
A2 - Fuhr, Norbert
A2 - Azzopardi, Leif
A2 - Hiemstra, Djoerd
PB - Springer Nature
Y2 - 14 April 2019 through 18 April 2019
ER -
