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Applying Heuristic and Machine Learning Strategies to Product Resolution

: Strauß, Oliver; Almheidat, Ahmad; Kett, Holger

Fulltext ()

Bozzon, Alessandro (Ed.) ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
WEBIST 2019, 15th International Conference on Web Information Systems and Technologies. Proceedings : September 18-20, 2019, Vienna, Austria
Setúbal: SciTePress, 2019
ISBN: 978-989-758-386-5
International Conference on Web Information Systems and Technologies (WEBIST) <15, 2019, Vienna>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Intelligente Produktdatenextraktion und Product Resolution
Conference Paper, Electronic Publication
Fraunhofer IAO ()

In order to analyze product data obtained from different web shops a process is needed to determine which product descriptions refer to the same product (product resolution). Based on string similarity metrics and existing product resolution approaches a new approach is presented with the following components: a) extraction of information from the unstructured product title extracted from the e-shops, b) inclusion of additional information in the matching process, c) a method to compute a product similarity metric from the available data, d) optimization and adaption of model parameters to the characteristics of the underlying data via a genetic algorithm and e) a framework to automatically evaluate the matching method on the basis of realistic test data. The approach achieved a precision of 0.946 and a recall of 0.673.