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