Conflict-Directed Relaxation of Constraints in Content-Based Recommender Systems

Content-based recommenders are systems that exploit detailed knowledge about the items in the catalog for generating adequate product proposals. In that context, query relaxation is one of the basic approaches for dealing with situations, where none of the products in the catalogue exactly matches the customer requirements. The major challenges when applying query relaxation are that the relaxation should be minimal (or optimal for the customer), that there exists a potentially vast search space, and that we have to deal with hard time constraints in interactive recommender applications. In this paper, we show how the task of finding adequate or customer optimal relaxations for a given recommendation problem can be efficiently achieved by applying techniques from the field of model-based diagnosis, i.e., with the help of extended algorithms for computing conflicts and hitting sets. In addition, we propose a best-effort search algorithm based on branch-and-bound for dealing with hard problems and also describe how an optimal relaxation can be immediately obtained when partial queries can be (pre-)evaluated. Finally, we discuss the results of an evaluation of the described techniques, which we made by extending an existing knowledge-based recommender system and which we based on different real-world problem settings.