  Besides the typical applications of recommender systems in B2C scenarios such
as movie or shopping platforms, there is a rising interest in transforming the
human-driven advice provided e.g. in consultancy via the use of recommender
systems. We explore the special characteristics of such knowledge-based B2B
services and propose a process that allows to incorporate recommender systems
into them. We suggest and compare several recommender techniques that allow to
incorporate the necessary contextual knowledge (e.g. company demographics).
These techniques are evaluated in isolation on a test set of business
intelligence consultancy cases. We then identify the respective strengths of
the different techniques and propose a new hybridisation strategy to combine
these strengths. Our results show that the hybridisation leads to a substantial
performance improvement over the individual methods.
