Constraint-based recommender systems: technologies and research issues

By Alexander Felfernig, Robin D. Burke. International Conference on Evolutionary Computation, 2008

309 citations recorded by the source corpus.

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Recommender systems survey (2013, background): “User models are based on knowledge structures such as querys (preferred features por products) [109], cases (case-based reasoning) [44], constraints (constraint-based reasoning) [74], ontologies [159], matching metrics and knowledge vectors [194], and social knowledge [53].”
Artificial intelligence in recommender systems (2020, background): “Another advantage is that users can impose constraints on the recommendation results [27].”
Recommender Systems (2016, unknown): “Constraint-based recommender systems are discussed in [196, 197].”
Mobile recommender systems in tourism (2014, background): “A considerable number of mobile RSs rely on user constraints and preferences, either explicitly stated or implicitly inferred, to derive content recommendations (Felfernig and Burke, 2008).”
Hybrid recommender systems: A systematic literature review (2017, background): “cars or houses) and manifest important constrains for the user (price) [5].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “2012) and constraint-based reasoning (Felfernig and Burke 2008).”