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

Author

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  • Lawrence Bunnell

    (Virginia Commonwealth University)

  • Kweku-Muata Osei-Bryson

    (Virginia Commonwealth University)

  • Victoria Y. Yoon

    (Virginia Commonwealth University)

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.

Suggested Citation

  • Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:6:d:10.1007_s10796-019-09935-9
    DOI: 10.1007/s10796-019-09935-9
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    References listed on IDEAS

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    1. Umberto Panniello & Michele Gorgoglione & Alexander Tuzhilin, 2016. "Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender Systems," Information Systems Research, INFORMS, vol. 27(1), pages 182-196, March.
    2. Y. Y. Yao, 1995. "Measuring retrieval effectiveness based on user preference of documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 46(2), pages 133-145, March.
    3. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    4. Wei-Lun Chang & Chien-Fang Jung, 2017. "A hybrid approach for personalized service staff recommendation," Information Systems Frontiers, Springer, vol. 19(1), pages 149-163, February.
    5. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    6. Chris Kimble & José Braga Vasconcelos & Álvaro Rocha, 2016. "Competence management in knowledge intensive organizations using consensual knowledge and ontologies," Information Systems Frontiers, Springer, vol. 18(6), pages 1119-1130, December.
    7. Ibrahim Muter & Tevfik Aytekin, 2017. "Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 405-421, August.
    8. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    9. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhangc, 2018. "Effects of Online Recommendations on Consumers’ Willingness to Pay," Information Systems Research, INFORMS, vol. 29(1), pages 84-102, March.
    10. Yan Li & Manoj A. Thomas & Kweku-Muata Osei-Bryson, 0. "Ontology-based data mining model management for self-service knowledge discovery," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    11. Stephen Burgess & Carmine Sellitto & Carmen Cox & Jeremy Buultjens, 2011. "Trust perceptions of online travel information by different content creators: Some social and legal implications," Information Systems Frontiers, Springer, vol. 13(2), pages 221-235, April.
    12. Wenge Rong & Baolin Peng & Yuanxin Ouyang & Kecheng Liu & Zhang Xiong, 2015. "Collaborative personal profiling for web service ranking and recommendation," Information Systems Frontiers, Springer, vol. 17(6), pages 1265-1282, December.
    13. James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Jyrki Wallenius & Stanley Zionts, 1992. "Multiple Criteria Decision Making, Multiattribute Utility Theory: The Next Ten Years," Management Science, INFORMS, vol. 38(5), pages 645-654, May.
    14. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2010. "Research Commentary --- Long Tails vs. Superstars: The Effect of Information Technology on Product Variety and Sales Concentration Patterns," Information Systems Research, INFORMS, vol. 21(4), pages 736-747, December.
    15. Yan Li & Manoj A. Thomas & Kweku-Muata Osei-Bryson, 2017. "Ontology-based data mining model management for self-service knowledge discovery," Information Systems Frontiers, Springer, vol. 19(4), pages 925-943, August.
    16. Palash Bera & Andrew Burton-Jones & Yair Wand, 2014. "Research Note ---How Semantics and Pragmatics Interact in Understanding Conceptual Models," Information Systems Research, INFORMS, vol. 25(2), pages 401-419, June.
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