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Title
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A gentle introduction to recommendation as counterfactual policy learning
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Author
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Abstract
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| The objective of this tutorial is to give a structured overview of the conceptual frameworks behind current state-of-the-art recommender systems, explain their underlying assumptions, the resulting methods and their shortcomings, and to introduce an exciting new class of approaches that frames the task of recommendation as a counterfactual policy learning problem. The tutorial can be divided into two modules. In module 1, participants learn about current approaches for building real-world recommender systems that comprise mainly of two frameworks, namely: recommendation as optimal auto-completion of user behaviour and recommendation as reward modelling. In module 2, we present the framework of recommendation as a counterfactual policy learning problem and go over the theoretical guarantees that address the shortcomings of the previous frameworks. We then proceed to go over the associated algorithms and test them against classical methods in RecoGym, an open-source recommendation simulation environment. Overall, we believe the subject of the course is extremely actual and fills a gap between the consecrated recommendation frameworks and the cutting edge research and sets the stage for future advances in the field. |
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Language
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English
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Source (book)
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UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, July, 2020, Genoa Italy
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Publication
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New York, N.Y.
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Association for Computing Machinery
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2020
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ISBN
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978-1-4503-6861-2
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DOI
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10.1145/3340631.3398666
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Volume/pages
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p. 392-393
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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