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Evaluating artificial short message service campaigns through rule based multi-instance multi-label classification
University of Borås, Faculty of Librarianship, Information, Education and IT. (InnovationLab)
University of Borås, Faculty of Librarianship, Information, Education and IT.
University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden.
2021 (English)In: AAAI-MAKE 2021Combining Machine Learning and Knowledge Engineering: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) / [ed] Andreas Martin; Knut Hinkelmann; Hans-Georg Fill; Aurona Gerber; Doug Lenat; Reinhard Stolle; Frank van Harmelen, CEUR-WS , 2021, Vol. 2846Conference paper, Published paper (Refereed)
Abstract [en]

Marketers need new ways of generating campaigns artificially for their marketing activities. Many marketers assume proprietary systems are individualized enough. This article investigates an order of models used to measure how reliably a system can generate campaigns artificially while producing a campaign classification and generation models that are integrated into an intelligent marketing system. The order is between a Classification Model (CM) and a Generation Model (GM). The order also functions as an iterative model improvement process for developing the models by evaluating the models' accuracy distributions. The CM received a mean accuracy of 100%. The GM received 98.9% mean accuracy and a reproducibility score of 96.2%, implying the vast potential for increased resource savings, marketing precision, and less consumer annoyance. The conclusion is that the developed system can reliantly construct campaigns.

Place, publisher, year, edition, pages
CEUR-WS , 2021. Vol. 2846
Keywords [en]
Artificial intelligence, Intelligent marketing system, Iterative model improvement, Classification (of information), Commerce, Machine learning, Springs (components), Classification models, Iterative model, Marketing activities, Multi label classification, Proprietary systems, Reproducibilities, Resource savings, Short message services, Marketing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hb:diva-25999Scopus ID: 2-s2.0-85104648031OAI: oai:DiVA.org:hb-25999DiVA, id: diva2:1579243
Conference
2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, AAAI-MAKE 2021, Stanford University, Palo Alto, California, USA, March 22-24, 2021.
Available from: 2021-07-08 Created: 2021-07-08 Last updated: 2023-11-27

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Sahlin, JohannesSundell, HåkanAlm, Håkan

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CiteExportLink to record
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Citation style
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