Zschech P, Heinrich K, Horn R, Hoeschele D (2019)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Publisher: Association for Information Systems
Conference Proceedings Title: Proceedings of the 25th Americas Conference on Information Systems
The task of mapping a domain-specific problem space to an adequate set of data mining (DM) methods is
a crucial step in data science projects. While there have been several efforts for automated method selection
in general, only few approaches consider the particularities of problem contexts expressed in domain-specific language. Therefore, we propose the concept of a text-based recommender system (TBRS) which
takes problem descriptions articulated in domain language as inputs and then recommends the best
suitable class of DM methods. Following a design science research methodology, the current focus is on the
initial steps of motivating the problem and conducting a requirements analysis. In particular, we outline
the problem setting using an exemplary scenario from industrial practice and derive requirements towards
an adequate solution artifact. Subsequently, we discuss potential TBRS methods with regard to requirement
fulfillment while organizing both methods and requirements in a structured framework. Finally, we
conclude the paper, discuss limitations and draw an outlook.
APA:
Zschech, P., Heinrich, K., Horn, R., & Hoeschele, D. (2019). Towards a Text-based Recommender System for Data Mining Method Selection. In Proceedings of the 25th Americas Conference on Information Systems. Cancún, MX: Association for Information Systems.
MLA:
Zschech, Patrick, et al. "Towards a Text-based Recommender System for Data Mining Method Selection." Proceedings of the 25th Americas Conference on Information Systems (AMCIS), Cancún Association for Information Systems, 2019.
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