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

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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.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II
EditorsMichelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Number of pages16
Volume2
PublisherSpringer Verlag
Publication date30.12.2017
Pages269-284
ISBN (print)978-3-319-71245-1
ISBN (electronic)978-3-319-71246-8
DOIs
Publication statusPublished - 30.12.2017
EventTHE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18.09.201722.09.2017
Conference number: 27
http://ecmlpkdd2017.ijs.si/

    Research areas

  • Informatics - Machine learning, Recommender systems, Contextual bandits, Recommendation, Dual optimization, Personalization

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