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

Maryam Tavakol; Ulf Brefeld

doi:10.1007/978-3-319-71246-8_17

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

Professur für Wirtschaftsinformatik, insbesondere Machine Learning

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

4 Zitate (Scopus)

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.

Originalsprache	Englisch
Titel	Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II
Redakteure/-innen	Michelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Seitenumfang	16
Band	2
Herausgeber (Verlag)	Springer Verlag
Erscheinungsdatum	30.12.2017
Seiten	269-284
ISBN (Print)	978-3-319-71245-1
ISBN (elektronisch)	978-3-319-71246-8
DOIs	https://doi.org/10.1007/978-3-319-71246-8_17
Publikationsstatus	Erschienen - 30.12.2017
Veranstaltung	ECML-PKDD 2017 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery - ECML PKDD 2017 - Skopje, Macedonia, Skopje, Mazedonien, ehemalige jugoslawische Republik Dauer: 19.09.2017 → 22.09.2017 Konferenznummer: 27 http://ecmlpkdd2017.org (Offizielle Event-Webseite) http://ecmlpkdd2017.ijs.si/

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10.1007/978-3-319-71246-8_17

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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 Verlag. https://doi.org/10.1007/978-3-319-71246-8_17

Tavakol, Maryam ; Brefeld, Ulf. / A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. 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 Verlag, 2017. S. 269-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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doi = "10.1007/978-3-319-71246-8\_17",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Michelangelo Ceci and Jaakko Hollmen and Ljupco Todorovski and Celine Vens and Saso Dzeroski",

booktitle = "Machine Learning and Knowledge Discovery in Databases",

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note = "ECML-PKDD 2017 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery - ECML PKDD 2017, ECML PKDD 2017 ; Conference date: 19-09-2017 Through 22-09-2017",

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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 Verlag, S. 269-284, ECML-PKDD 2017 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery - ECML PKDD 2017, Skopje, Mazedonien, ehemalige jugoslawische Republik, 19.09.17. https://doi.org/10.1007/978-3-319-71246-8_17

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 Verlag, 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 Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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 - https://www.scopus.com/pages/publications/85040222425

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 Verlag

T2 - ECML-PKDD 2017 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery - ECML PKDD 2017

Y2 - 19 September 2017 through 22 September 2017

ER -

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 Verlag. 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

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

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