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Context-Aware Recommender Systems: From Foundations to Recent Developments

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Recommender Systems Handbook

Abstract

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce, personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. Prior work has extensively demonstrated that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and major approaches to modeling it in recommender systems, including explicit vs. latent and static vs. dynamic approaches. We also discuss three popular algorithmic paradigms—contextual pre-filtering, post-filtering, and modeling—for incorporating contextual information into the recommendation process, and survey the recent advances in contextual modeling that include tensor factorization, deep learning, and reinforcement learning techniques. We also discuss important directions for future research.

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Notes

  1. 1.

    https://link.springer.com/conference/context.

  2. 2.

    https://www.merriam-webster.com/dictionary/context.

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Authors and Affiliations

  1. Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

    Gediminas Adomavicius

  2. Fox School of Business, Temple University, Philadelphia, PA, USA

    Konstantin Bauman

  3. Stern School of Business, New York University, New York, NY, USA

    Alexander Tuzhilin & Moshe Unger

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  1. Gediminas Adomavicius
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  2. Konstantin Bauman
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  3. Alexander Tuzhilin
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  4. Moshe Unger
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Correspondence to Konstantin Bauman .

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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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Adomavicius, G., Bauman, K., Tuzhilin, A., Unger, M. (2022). Context-Aware Recommender Systems: From Foundations to Recent Developments . 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_6

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