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Authors: Hans Friedrich Witschel and Andreas Martin

Affiliation: FHNW University of Applied Sciences and Arts Northwestern Switzerland, CH-4600 Olten and Switzerland

Keyword(s): Recommender Systems, Knowledge Representation, Random Walks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Intelligent Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Learning Organization & Organizational Learning ; Symbolic Systems ; Tools and Technology for Knowledge Management

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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Paper citation in several formats:
Witschel, H. and Martin, A. (2018). Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KMIS; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 63-72. DOI: 10.5220/0006893900630072

@conference{kmis18,
author={Hans Friedrich Witschel. and Andreas Martin.},
title={Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KMIS},
year={2018},
pages={63-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006893900630072},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KMIS
TI - Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders
SN - 978-989-758-330-8
IS - 2184-3228
AU - Witschel, H.
AU - Martin, A.
PY - 2018
SP - 63
EP - 72
DO - 10.5220/0006893900630072
PB - SciTePress

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