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.

翻译：除了在电影或购物平台等B2C情景中建议系统典型应用外,人们越来越有兴趣改变由人驱动的建议,例如通过使用建议系统提供咨询。我们探索这种以知识为基础的B2B服务的特殊性,并提议一个允许将建议系统纳入其中的程序。我们建议并比较几种建议技术,以便能够纳入必要的背景知识(如公司人口统计)。这些技术是在一套测试商业情报咨询个案中单独评估的。我们然后确定不同技术各自的优势,并提出新的混合战略,以结合这些优势。我们的结果表明,混合化导致与个别方法相比业绩的显著改善。