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Constraint-based recommenders support users in the identification of items(products) fitting their wishes and needs. Example domains are financialservices and electronic equipment. In this paper we show how divide-and-conquerbased (direct) diagnosis algorithms (no conflict detection is needed) can beexploited in constraint-based recommendation scenarios. In this context, weprovide an overview of the MediaWiki-based recommendation environment WeeVis.

An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment