RLRecommender: A Representation-Learning-Based Recommendation Method for Business Process Modeling View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-07

AUTHORS

Huaqing Wang , Lijie Wen , Li Lin , Jianmin Wang

ABSTRACT

Most traditional business process recommendation methods cannot deal with complex structures such as interacting loops, and they cannot handle large complex datasets with a great quantity of processes and activities. To address these issues, RLRecommender, a method based on representation learning, is proposed. RLRecommender extracts three kinds of relation sets from the models, both activities and relations between them are projected into a continuous low-dimensional space, and proper activity nodes are recommended by comparing the distances in the space. The experimental results show that our method not only outperforms other baselines on small dataset, but also performs effectively on large dataset. More... »

PAGES

478-486

Book

TITLE

Service-Oriented Computing

ISBN

978-3-030-03595-2
978-3-030-03596-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-03596-9_34

DOI

http://dx.doi.org/10.1007/978-3-030-03596-9_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1108060008


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