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.

本文研究在特定于图形域的建模语言（DSML）中使用推荐系统。在图形化DSML中使用推荐系统的目的是要克服主动建模的缺点，即建模者必须在无法自动确定要执行的下一个建模动作（例如，添加，删除或编辑）时告知模型智能引擎如何前进。 ）。为了评估我们的目标，我们在Proactive Modeling Engine中实施了一个推荐系统，该系统是Generic Modeling Environment的附加组件。然后，我们进行了实验，主观和客观地评估了Proactive Modeling Engine的增强功能。我们的实验结果表明，使用推荐系统扩展主动建模的平均倒数排名为0.871。同样