Random walks on human knowledge: incorporating human knowledge into data-driven recommenders
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Author (Corporation)
Publication date
2018
Typ of student thesis
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Collections
Type
04B - Conference paper
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Parent work
IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings
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Volume
3
Issue / Number
Pages / Duration
61-70
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Publisher / Publishing institution
Place of publication / Event location
Seville
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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.
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Subject (DDC)
Event
10th International Conference on Knowledge Management and Information Sharing (KMIS)
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ISBN
978-989-758-330-8
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Closed
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Citation
Witschel, H. F., & Martin, A. (2018). Random walks on human knowledge: incorporating human knowledge into data-driven recommenders. In J. Bernardino, A. Salgado, & J. Filipe (Eds.), IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings (Vol. 3, pp. 61–70). https://doi.org/10.5220/0006893900630072