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Using recommender systems to improve proactive modeling

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Abstract

This article investigates using recommender systems within graphical domain-specific modeling languages (DSMLs). The objective of using recommender systems within a graphical DSML is to overcome a shortcoming of proactive modeling where the modeler must inform the model intelligence engine how to progress when it cannot automatically determine the next modeling action to execute (e.g., add, delete, or edit). To evaluate our objective, we implemented a recommender system into the Proactive Modeling Engine, which is an add-on for the Generic Modeling Environment. We then conducted experiments to subjectively and objectively evaluate enhancements to the Proactive Modeling Engine. The results of our experiments show that extending proactive modeling with a recommender system results in an average reciprocal hit-rank of 0.871. Likewise, the enhancements yield a System Usability Scale rating of 77. Finally, user feedback shows that integrating recommender systems into DSMLs increases usability and learnability.

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Notes

  1. The tutorial for installing the software is located at the following location: https://github.com/SEDS/GAME/wiki/GAME-for-GME-Installation

  2. The tutorial about the recommender system is located at https://github.com/SEDS/GAME/wiki/Tutorial-on-Working-with-GAME-Model-Intelligence

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Authors and Affiliations

  1. Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Arvind Nair & James H. Hill

  2. Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Xia Ning

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  1. Arvind Nair
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  2. Xia Ning
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Correspondence to James H. Hill.

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Communicated by Jordi Cabot.

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Nair, A., Ning, X. & Hill, J.H. Using recommender systems to improve proactive modeling. Softw Syst Model 20, 1159–1181 (2021). https://doi.org/10.1007/s10270-020-00841-2

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