@article{
 title = {New Hybrid Techniques for Business Recommender Systems},
 type = {article},
 year = {2022},
 volume = {12},
 websites = {https://www.mdpi.com/2076-3417/12/10/4804},
 id = {774bea80-b55c-3015-8739-715cfa11f711},
 created = {2022-08-22T15:10:35.209Z},
 file_attached = {false},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:35.209Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {app12104804},
 source_type = {article},
 private_publication = {false},
 abstract = {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 incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating 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 substantial performance improvement over the individual methods.},
 bibtype = {article},
 author = {Pande, Charuta and Witschel, Hans Friedrich and Martin, Andreas},
 doi = {10.3390/app12104804},
 journal = {Applied Sciences},
 number = {10}
}

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