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Novelty and Diversity in Recommender Systems

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Recommender Systems Handbook

Abstract

Novelty and diversity have been identified, along with accuracy, as foremost properties of useful recommendations. Considerable progress has been made in the field in terms of the definition of methods to enhance such properties, as well as methodologies and metrics to assess how well such methods work. In this chapter we give an overview of the main contributions to this area in the field of recommender systems, and seek to relate them together in a unified view, analyzing the common elements underneath the different forms under which novelty and diversity have been addressed, and identifying connections to closely related work on diversity in other fields.

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Notes

  1. 1.

    Of course, what “interaction” means and to what extent it will inhibit future recommendations is application-dependent, e.g. an online store may recommend an item the user has inspected but not bought.

  2. 2.

    Note that we normalize IUD by \(\vert \mathcal{U}\vert - 1\) because all items in R are recommended to at least one user (the target of R), therefore if we normalized by \(\vert \mathcal{U}\vert\), the value of the metric for the optimal recommendation would be \((\vert \mathcal{U}\vert - 1)/\vert \mathcal{U}\vert <1\). Put in another way, \(v \in \mathcal{U}\) in the numerator could be as well written as \(v \in \mathcal{U}-\{ u\}\), which would call for normalizing by \(\vert \mathcal{U}-\{ u\}\vert = \vert \mathcal{U}\vert - 1\). The difference is negligible in practice though, and we believe both forms of normalization would be acceptable. The same rationale applies to Eq. (26.6) below.

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  1. Universidad Autonoma de Madrid, Madrid, Spain

    Pablo Castells & Saul Vargas

  2. University College Dublin, Dublin, Ireland

    Neil J. Hurley

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    Francesco Ricci

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

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Castells, P., Hurley, N.J., Vargas, S. (2015). Novelty and Diversity in Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_26

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