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

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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.
OriginalspracheEnglisch
TitelMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II
HerausgeberMichelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Anzahl der Seiten16
Band2
VerlagSpringer Verlag
Erscheinungsdatum30.12.2017
Seiten269-284
ISBN (Print)978-3-319-71245-1
ISBN (elektronisch)978-3-319-71246-8
DOIs
PublikationsstatusErschienen - 30.12.2017
VeranstaltungTHE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017 - Skopje, Mazedonien, ehemalige jugoslawische Republik
Dauer: 18.09.201722.09.2017
Konferenznummer: 27
http://ecmlpkdd2017.ijs.si/

DOI

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