research-article

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

Authors:

Lawrence Bunnell,

Kweku-Muata Osei-Bryson,

Victoria Y. YoonAuthors Info & Claims

Information Systems Frontiers, Volume 22, Issue 6

Pages 1377 - 1418

https://doi.org/10.1007/s10796-019-09935-9

Published: 01 December 2020 Publication History

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.

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Cited By

Freller LKeck MNeumayr TAugstein MBottoni PPanizzi E(2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3531073.3534484
Zuo MAngelopoulos SLiang ZOu C(2022)Blazing the Trail: Considering Browsing Path Dependence in Online Service Response StrategyInformation Systems Frontiers10.1007/s10796-022-10311-325:4(1605-1619)Online publication date: 21-Jul-2022
https://dl.acm.org/doi/10.1007/s10796-022-10311-3
Heinrich BHopf MLohninger DSchiller ASzubartowicz M(2022)Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender SystemsInformation Systems Frontiers10.1007/s10796-020-10071-y24:1(267-286)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10796-020-10071-y

Index Terms

RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image Information Systems Frontiers

Information Systems Frontiers Volume 22, Issue 6

Dec 2020

296 pages

ISSN:1387-3326

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© Springer Science+Business Media, LLC, part of Springer Nature 2019.

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Published: 01 December 2020

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Cited By

Freller LKeck MNeumayr TAugstein MBottoni PPanizzi E(2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3531073.3534484
Zuo MAngelopoulos SLiang ZOu C(2022)Blazing the Trail: Considering Browsing Path Dependence in Online Service Response StrategyInformation Systems Frontiers10.1007/s10796-022-10311-325:4(1605-1619)Online publication date: 21-Jul-2022
https://dl.acm.org/doi/10.1007/s10796-022-10311-3
Heinrich BHopf MLohninger DSchiller ASzubartowicz M(2022)Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender SystemsInformation Systems Frontiers10.1007/s10796-020-10071-y24:1(267-286)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10796-020-10071-y

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