RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers

Abstract

Scholarly research has extensively examined a number of issues and challenges affecting recommender systems (e.g. ‘cold-start’, ‘scrutability’, ‘trust’, ‘context’, etc.). However, a comprehensive knowledge classification of the issues involved with recommender systems research has yet to be developed. A holistic knowledge representation of the issues affecting a domain is critical for research advancement. The aim of this study is to advance scholarly research within the domain of recommender systems through formal knowledge classification of issues and their relationships to one another within recommender systems research literature. In this study, we employ a rigorous ontology engineering process for development of a recommender system issues ontology. This ontology provides a formal specification of the issues affecting recommender systems research and development. The ontology answers such questions as, “What issues are associated with ‘trust’ in recommender systems research?”, “What are issues associated with improving and evaluating the ‘performance’ of a recommender system?” or “What ‘contextual’ factors might a recommender systems developer wish to consider in order to improve the relevancy and usefulness of recommendations?” Additionally, as an intermediate representation step in the ontology acquisition process, a concept map of recommender systems issues has been developed to provide conceptual visualization of the issues so that researchers may discern broad themes as well as relationships between concepts. These knowledge representations may aid future researchers wishing to take an integrated approach to addressing the challenges and limitations associated with current recommender systems research.