Developing Constraint-based Recommenders A. Felfernig and G. Friedrich and D. Jannach and M. Zanker Abstract Recommender systems provide valuable support for users who are search- ing for products and services in e-commerce environments. Research in the field long focused on algorithms supporting the recommendation of quality & taste prod- ucts such as news, books, or movies. Nowadays, the scope of those systems is ex- tended to complex product domains such as financial services or electronic con- sumer goods. Constraint-based recommenders are particularly well suited as they support effective product and service selection processes in such domains. In this chapter, we characterize constraint-based recommendation problems and provide an overview of major technologies that support the development of knowledge bases for constraint-based recommenders which is of high importance for a successful application in commercial settings. Thereafter we give an overview of intelligent interaction mechanisms which are supported by constraint-based recommender ap- plications, discuss scenarios where constraint-based recommenders have been suc- cessfully applied, and provide a discussion of different solution approaches. Finally, this chapter is concluded with an outline of open research issues. Alexander Felfernig Graz University of Technology e-mail: alexander.felfernig@ist.tugraz.at Gerhard Friedrich University Klagenfurt e-mail: gerhard.friedrich@uni-klu.ac.at Dietmar Jannach TU Dortmund e-mail: dietmar.jannach@tu-dortmund.de Markus Zanker University Klagenfurt e-mail: markus.zanker@uni-klu.ac.at 1