Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence

Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence

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, de...

Full description

Bibliographic Details
Main Authors: Michael Fellmann, Novica Zarvić, Oliver Thomas
Format: Article
Language:English
Published: Riga Technical University 2018-04-01
Series:Complex Systems Informatics and Modeling Quarterly
Subjects:
Recommender systems
Semantic modeling
Process-Oriented information system
Empirical evaluation
Experiment
Online Access:https://csimq-journals.rtu.lv/article/view/2122
id doaj-53405d31fb384b899a57cc9bcbaa2abc
record_format Article
spelling doaj-53405d31fb384b899a57cc9bcbaa2abc2020-11-25T00:10:48ZengRiga Technical UniversityComplex Systems Informatics and Modeling Quarterly2255-99222018-04-01014647910.7250/csimq.2018-14.051148Business Processes Modeling Recommender Systems: User Expectations and Empirical EvidenceMichael Fellmann0Novica Zarvić1Oliver Thomas2Institute of Computer Science, University of Rostock, RostockInformation Management and Information Systems Group, Osnabrück University, OsnabrückInformation Management and Information Systems Group, Osnabrück University, OsnabrückRecommender 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.https://csimq-journals.rtu.lv/article/view/2122Recommender systemsSemantic modelingProcess-Oriented information systemEmpirical evaluationExperiment
collection DOAJ
language English
format Article
sources DOAJ
author Michael Fellmann
Novica Zarvić
Oliver Thomas
spellingShingle Michael Fellmann
Novica Zarvić
Oliver Thomas
Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
Complex Systems Informatics and Modeling Quarterly
Recommender systems
Semantic modeling
Process-Oriented information system
Empirical evaluation
Experiment
author_facet Michael Fellmann
Novica Zarvić
Oliver Thomas
author_sort Michael Fellmann
title Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
title_short Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
title_full Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
title_fullStr Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
title_full_unstemmed Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence
title_sort business processes modeling recommender systems: user expectations and empirical evidence
publisher Riga Technical University
series Complex Systems Informatics and Modeling Quarterly
issn 2255-9922
publishDate 2018-04-01
description 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.
topic Recommender systems
Semantic modeling
Process-Oriented information system
Empirical evaluation
Experiment
url https://csimq-journals.rtu.lv/article/view/2122
work_keys_str_mv AT michaelfellmann businessprocessesmodelingrecommendersystemsuserexpectationsandempiricalevidence
AT novicazarvic businessprocessesmodelingrecommendersystemsuserexpectationsandempiricalevidence
AT oliverthomas businessprocessesmodelingrecommendersystemsuserexpectationsandempiricalevidence
_version_ 1725407026547261440

Similar Items