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Learning Preference Models in Recommender Systems

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Preference Learning

Abstract

As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of nonrelevant content according to the information stored in a user profile, recommendation algorithms process information about user interests – acquired in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way – and exploit these data to generate a list of recommended items. Although each type of filtering method has its own weaknesses and strengths, preference handling is one of the core issues in the design of every recommender system: since these systems aim to guide users in a personalized way to interesting or useful objects in a large space of possible options, it is important for them to accurately capture and model user preferences. The goal of this chapter is to provide a general overview of the approaches to learning preference models in the context of recommender systems. In the first part, we introduce general concepts and terminology of recommender systems, giving a brief analysis of advantages and drawbacks for each filtering approach. Then we will deal with the issue of learning preference models, show the most popular techniques for profile learning and preference elicitation, and analyze methods for feedback gathering in recommender systems.

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Notes

  1. 1.

    The Behavior Category (Examine, Retain, Reference, Annotate, and Create) refers to the underlying purpose of the observed behavior.

  2. 2.

    Minimum Scope (Segment, Object, and Class) refers to the smallest possible scope of the item being acted upon.

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Authors and Affiliations

  1. Department of Computer Science, University of Bari “Aldo Moro”, Bari, Italy

    Marco de Gemmis, Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci & Giovanni Semeraro

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  1. Marco de Gemmis
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  2. Leo Iaquinta
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  3. Pasquale Lops
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  4. Cataldo Musto
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  5. Fedelucio Narducci
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  6. Giovanni Semeraro
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Correspondence to Marco de Gemmis .

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Editors and Affiliations

  1. FB Informatik, TU Darmstadt, Hochschulstr. 10, Darmstadt, 64289, Germany

    Johannes Fürnkranz

  2. FB Mathematik und Informatik, Philipps-Universität Marburg, Hans-Meerwein-Str., Marburg, 35032, Germany

    Eyke Hüllermeier

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Gemmis, M.d., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G. (2010). Learning Preference Models in Recommender Systems. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_18

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  • DOI: https://doi.org/10.1007/978-3-642-14125-6_18

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