Relevancy Scoring for Knowledge-based Recommender Systems

Robert David, Trineke Kamerling

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.

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