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Recommender Systems Handbook pp 187–215Cite as

Developing Constraint-based Recommenders

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Abstract

Traditional recommendation approaches (content-based filtering [48] and collaborative filtering[40]) are well-suited for the recommendation of quality&taste products such as books, movies, or news. However, especially in the context of products such as cars, computers, appartments, or financial services those approaches are not the best choice (see also Chapter 11). For example, apartments are not bought very frequently which makes it rather infeasible to collect numerous ratings for one specific item (exactly such ratings are required by collaborative recommendation algorithms). Furthermore, users of recommender applications would not be satisfied with recommendations based on years-old item preferences (exactly such preferences would be exploited in this context by content-based filtering algorithms).

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Author information

Authors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  2. University Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich & Markus Zanker

  3. TU Dortmund, Dortmund, Germany

    Dietmar Jannach

Authors
  1. Alexander Felfernig
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  2. Gerhard Friedrich
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  3. Dietmar Jannach
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  4. Markus Zanker
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Corresponding author

Correspondence to Alexander Felfernig .

Editor information

Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Felfernig, A., Friedrich, G., Jannach, D., Zanker, M. (2011). Developing Constraint-based Recommenders. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_6

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