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Session-Based Recommender Systems

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Abstract

Session-based recommendation is concerned with the problem of tailoring item suggestions according to the short-term needs and assumed intents of the user.

The input in this recommendation scenario consists of an often very short sequence of user interactions that are observed in an ongoing usage session, and in many cases longer-term preferences of the users are not available. Such problems are highly relevant in practice because (i) recommendations should often be made also to anonymous and first-time users and because (ii) the users’ intents can change from session to session. In this chapter, we first elaborate on practical application scenarios for session-based recommender systems, provide a characterization of the problem class, and outline key challenges. Afterwards, we review technical approaches to session-based recommendation and report common practices of evaluating such systems. The chapter ends with a discussion of open challenges and an outlook on future directions in the area.

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Notes

  1. 1.

    Not connecting click data with individual user accounts may in some cases also be beneficial in terms of user privacy and the General Data Protection Regulation in the European Union.

  2. 2.

    These discussions apply both for session-based and for session-aware recommender systems. For the sake of brevity, we will use the term session-based in this chapter, and will explicitly state when not both types of problems are meant.

  3. 3.

    For the sake of readability, we use the terms “user action (on an item)” and “user-item interaction” interchangeably.

  4. 4.

    https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn.html (Date visited: 2020/06/01).

  5. 5.

    Bias terms are omitted to enhance readability.

  6. 6.

    This should not to be confused with the common definition of context in recommender systems, which often refers to the set of contextual variables, such as location, time, etc., that can influence a user’s decisions.

  7. 7.

    https://github.com/rn5l/session-rec.

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Authors and Affiliations

  1. University of Klagenfurt, Klagenfurt, Austria

    Dietmar Jannach

  2. Pandora Media LLC, Oakland, CA, USA

    Massimo Quadrana

  3. Politecnico di Milano, Milan, Italy

    Paolo Cremonesi

Authors
  1. Dietmar Jannach
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  2. Massimo Quadrana
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  3. Paolo Cremonesi
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Correspondence to Dietmar Jannach .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Jannach, D., Quadrana, M., Cremonesi, P. (2022). Session-Based Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_8

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