@inproceedings{
 title = {Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders},
 type = {inproceedings},
 year = {2018},
 keywords = {Knowledge Representation,Knowledge representation,Random Walks,Random walks,Recommender Systems,Recommender systems},
 pages = {63-72},
 volume = {3},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006893900630072},
 publisher = {SCITEPRESS - Science and Technology Publications},
 institution = {INSTICC},
 id = {8280970d-e765-3191-ac2f-32921801cb02},
 created = {2022-08-22T10:15:22.095Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 last_modified = {2022-08-22T12:28:05.686Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {kmis18},
 source_type = {conference},
 folder_uuids = {b35e5b0c-63da-4577-ad8a-01a619ce7c0b},
 private_publication = {false},
 abstract = {We explore the use of recommender systems in business scenarios such as consultancy. In these situations, apart from personal preferences of users, knowledge about objective business-driven criteria plays a role. We investigate strategies for representing and incorporating such knowledge into data-driven recommenders. As a baseline, we choose a robust and flexible paradigm that is based on a simple graph-based representation of past customer cases and choices, in combination with biased random walks. On a real data set from a business intelligence consultancy firm, we study how the incorporation of two important types of explicit human knowledge – namely taxonomic and associative knowledge – impacts the effectiveness of a data-driven recommender. Our results show no consistent improvement for taxonomic knowledge, but quite substantial and significant gains when using associative knowledge.},
 bibtype = {inproceedings},
 author = {Witschel, H.F. Hans Friedrich and Martin, Andreas},
 doi = {10.5220/0006893900630072},
 booktitle = {Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}
}

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