Standard
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. /
Tavakol, Maryam; Brefeld, Ulf.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. ed. / Michelangelo Ceci; Jaakko Hollmen; Ljupco Todorovski; Celine Vens; Saso Dzeroski. Vol. 2 Springer, 2017. p. 269-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10535 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Tavakol, M & Brefeld, U 2017,
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. in M Ceci, J Hollmen, L Todorovski, C Vens & S Dzeroski (eds),
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. vol. 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10535 LNAI, Springer, pp. 269-284, THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017, Skopje, Macedonia, The Former Yugoslav Republic of,
18.09.17.
https://doi.org/10.1007/978-3-319-71246-8_17
APA
Tavakol, M., & Brefeld, U. (2017).
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. In M. Ceci, J. Hollmen, L. Todorovski, C. Vens, & S. Dzeroski (Eds.),
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II (Vol. 2, pp. 269-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10535 LNAI). Springer.
https://doi.org/10.1007/978-3-319-71246-8_17
Vancouver
Tavakol M, Brefeld U.
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. In Ceci M, Hollmen J, Todorovski L, Vens C, Dzeroski S, editors, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Vol. 2. Springer. 2017. p. 269-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71246-8_17
Bibtex
@inbook{0f45dbe652f64731ab63d83ca20dbbef,
title = "A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations",
abstract = "We present a unified contextual bandit framework for recommendation problems that is able to capture long- and short-term interests of users. The model is devised in dual space and the derivation is consequentially carried out using Fenchel-Legrende conjugates and thus leverages to a wide range of tasks and settings.We detail two instantiations for regression and classification scenarios and obtain well-known algorithms for these special cases. The resulting general and unified framework allows for quickly adapting contextual bandits to different applications at-hand. The empirical study demonstrates that the proposed long- and short-term framework outperforms both, short-term and long-term models on data. Moreover, a tweak of the combined model proves beneficial in cold start problems.",
keywords = "Informatics, Machine learning, Recommender systems, Contextual bandits, Recommendation, Dual optimization, Personalization",
author = "Maryam Tavakol and Ulf Brefeld",
year = "2017",
month = dec,
day = "30",
doi = "10.1007/978-3-319-71246-8_17",
language = "English",
isbn = "978-3-319-71245-1",
volume = "2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "269--284",
editor = "Michelangelo Ceci and Jaakko Hollmen and Ljupco Todorovski and Celine Vens and Saso Dzeroski",
booktitle = "Machine Learning and Knowledge Discovery in Databases",
address = "Germany",
note = "THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
url = "http://ecmlpkdd2017.ijs.si/",
}
RIS
TY - CHAP
T1 - A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations
AU - Tavakol, Maryam
AU - Brefeld, Ulf
N1 - Conference code: 27
PY - 2017/12/30
Y1 - 2017/12/30
N2 - We present a unified contextual bandit framework for recommendation problems that is able to capture long- and short-term interests of users. The model is devised in dual space and the derivation is consequentially carried out using Fenchel-Legrende conjugates and thus leverages to a wide range of tasks and settings.We detail two instantiations for regression and classification scenarios and obtain well-known algorithms for these special cases. The resulting general and unified framework allows for quickly adapting contextual bandits to different applications at-hand. The empirical study demonstrates that the proposed long- and short-term framework outperforms both, short-term and long-term models on data. Moreover, a tweak of the combined model proves beneficial in cold start problems.
AB - We present a unified contextual bandit framework for recommendation problems that is able to capture long- and short-term interests of users. The model is devised in dual space and the derivation is consequentially carried out using Fenchel-Legrende conjugates and thus leverages to a wide range of tasks and settings.We detail two instantiations for regression and classification scenarios and obtain well-known algorithms for these special cases. The resulting general and unified framework allows for quickly adapting contextual bandits to different applications at-hand. The empirical study demonstrates that the proposed long- and short-term framework outperforms both, short-term and long-term models on data. Moreover, a tweak of the combined model proves beneficial in cold start problems.
KW - Informatics
KW - Machine learning
KW - Recommender systems
KW - Contextual bandits
KW - Recommendation
KW - Dual optimization
KW - Personalization
UR - http://ecmlpkdd2017.ijs.si/papers/paperID239.pdf
UR - http://www.scopus.com/inward/record.url?scp=85040222425&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71246-8_17
DO - 10.1007/978-3-319-71246-8_17
M3 - Article in conference proceedings
SN - 978-3-319-71245-1
VL - 2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 269
EP - 284
BT - Machine Learning and Knowledge Discovery in Databases
A2 - Ceci, Michelangelo
A2 - Hollmen, Jaakko
A2 - Todorovski, Ljupco
A2 - Vens, Celine
A2 - Dzeroski, Saso
PB - Springer
T2 - THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017
Y2 - 18 September 2017 through 22 September 2017
ER -
