Constraint-Based Recommender Systems



Recommender systems provide valuable support for users who are searching for products in e-commerce environments. Research in the field long focused on rating-based algorithms supporting the recommendation of quality and taste products such as news, books, or movies. The recommendation of more complex products such as financial services or electronic consumer goods however requires additional types of knowledge to be encoded in a recommender system. Constraint-based approaches are particularly well suited and can make the product selection process more effective in such domains. In this chapter, we review constraint-based recommendation approaches and provide an overview of technologies for the development of knowledge bases for constraint-based recommenders since appropriate tool support can be crucial in practical settings. We furthermore discuss possible forms of user interaction that are supported by constraint-based recommender applications, report scenarios in which constraint-based recommenders have been successfully applied, and review different technical solution approaches. An outline of possible directions for future research concludes this chapter.


Recommender System Domain Expert Constraint Satisfaction Problem Conjunctive Query Preference Elicitation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Graz University of TechnologyGrazAustria
  2. 2.Alpen-Adria-Universitaet KlagenfurtKlagenfurtAustria
  3. 3.TU DortmundDortmundGermany

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