Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence

Michael Fellmann, Novica Zarvić, Oliver Thomas


Recommender systems are in widespread use in many areas, especially electronic commerce solutions. In this contribution, we apply recommender functionalities to business process modeling and investigate their potential for supporting process modeling. To do so, we have implemented two prototypes, demonstrated them at a major fair and collected user feedback. After analysis of the feedback, we have confronted the findings with the results of the experiment. Our results indicate that fairgoers expect increased modeling speed as the key advantage and completeness of models as the most unlikely advantage. This stands in contrast to an initial experiment revealing that modelers, in fact, increase the completeness of their models when adequate knowledge is presented while time consumption is not necessarily reduced. We explain possible causes of this mismatch and finally hypothesize on two “sweet spots” of process modeling recommender systems.


Recommender systems; Semantic modeling; Process-Oriented information system; Empirical evaluation; Experiment

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DOI: 10.7250/csimq.2018-14.05


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