RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers
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RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers Lawrence Bunnell 1
&
Kweku-Muata Osei-Bryson 1 & Victoria Y. Yoon 1
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Scholarly research has extensively examined a number of issues and challenges affecting recommender systems (e.g. ‘coldstart’, ‘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. Keywords Recommender systems issues . Recommendation agents . Thematic analysis . Concept mapping . Ontology acquisition
1 Introduction Recommender systems have become an integral part of our everyday lives. Today, most people are familiar with recommender systems through one or more numerous ecommerce applications such as NetFlix®, Pandora® and Spotify® for entertainment, Match® and eHarmony® for online dating or Amazon® and eBay® for product recommendations. Originally, the general idea behind recommender systems was to retrieve only the most relevant and useful items from a wide array of choices and thereby assist in reducing cognitive overload (Yoon et al. 2013). In the era of “big-data”, the
* Lawrence Bunnell [email protected] 1
Department of Information Systems, Virginia Commonwealth University, Richmond, VA 23284, USA
amount of information available to consumers makes it increasingly difficult for users of information systems (IS) to digest and analyze all of the available choices. In this environment, recommender systems provide a practical means of 1) w
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