Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders

Hans Friedrich Witschel, Andreas Martin

2018

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.

Paper Citation

in Harvard Style

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) - Volume 3: KMIS; ISBN 978-989-758-330-8, SciTePress, pages 63-72. DOI: 10.5220/0006893900630072

in Bibtex Style

@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) - Volume 3: KMIS},
year={2018},
pages={63-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006893900630072},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS
TI - Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders
SN - 978-989-758-330-8
AU - Witschel H.
AU - Martin A.
PY - 2018
SP - 63
EP - 72
DO - 10.5220/0006893900630072
PB - SciTePress