A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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. Hrsg. / Michelangelo Ceci; Jaakko Hollmen; Ljupco Todorovski; Celine Vens; Saso Dzeroski. Band 2 Springer, 2017. S. 269-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10535 LNAI).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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 (Hrsg.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Bd. 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10535 LNAI, Springer, S. 269-284, THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017, Skopje, Mazedonien, ehemalige jugoslawische Republik, 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 (Hrsg.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II (Band 2, S. 269-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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, Hrsg., Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Band 2. Springer. 2017. S. 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 -

DOI