Relevancy Scoring for Knowledge-based Recommender Systems

Robert David, Trineke Kamerling

2019

Abstract

Knowledge-based recommender systems are well suited for users to explore complex knowledge domains like iconography without having domain knowledge. To help them understand and make decisions for navigation in the information space, we can show how important specific concept annotations are for the description of an item in a collection. We present an approach to automatically determine relevancy scores for concepts of a domain model. These scores represent the importance for item descriptions as part of knowledge-based recommender systems. In this paper we focus on the knowledge domain of iconography, which is quite complex, difficult to understand and not commonly known. The use case for a knowledge-based recommender system in this knowledge domain is the exploration of a museum collection of historical artworks. The relevancy scores for the concepts of an artwork should help the user to understand the iconographic interpretation and to navigate the collection based on personal interests.

Paper Citation

in Harvard Style

David R. and Kamerling T. (2019). Relevancy Scoring for Knowledge-based Recommender Systems. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD; ISBN 978-989-758-382-7, SciTePress, pages 233-239. DOI: 10.5220/0008068602330239

in Bibtex Style

@conference{keod19,
author={Robert David and Trineke Kamerling},
title={Relevancy Scoring for Knowledge-based Recommender Systems},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD},
year={2019},
pages={233-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008068602330239},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD
TI - Relevancy Scoring for Knowledge-based Recommender Systems
SN - 978-989-758-382-7
AU - David R.
AU - Kamerling T.
PY - 2019
SP - 233
EP - 239
DO - 10.5220/0008068602330239
PB - SciTePress