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Matching product offers of e-shops

: Horch, Andrea; Kett, Holger; Weisbecker, Anette


Cao, Huiping (Ed.); Li, Jinyan (Ed.); Wang. Ruili (Ed.):
Trends and applications in knowledge discovery and data mining : PAKDD 2016 workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19-22, 2016, revised selected papers
Cham: Springer, 2016 (Lecture notes in computer science 9794)
ISBN: 978-3-319-42995-3 (print)
ISBN: 978-3-319-42996-0 (eBook)
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <20, 2016, Auckland>
Workshop on Biologically Inspired Data Mining Techniques (BDM) <2016, Auckland>
Workshop on Machine Learning for Sensory Data Analysis (MLSDA) <2016, Auckland>
Workshop on Predictive Analytics for Critical Care (PACC) <2016, Auckland>
Workshop on Data Mining in Business and Finance (WDMBF) <2016, Auckland>
Fraunhofer IAO ()

E-commerce is a continuously growing and competitive market. There are several motivations for e-shoppers, sellers and manufacturers to require an automated approach for matching product offers from various online sources referring to the same or a similar real-world product. Currently, there are several approaches for the assignment of identical and similar product offers. These existing approaches are not sufficient for performing a precise comparison as they only return a similarity value for two compared products but do not give any information for further calculations and analyses. The contribution of this paper is a novel approach and an algorithm for matching identical and very similar product offers based on the pairwise comparison of the product names. For this purpose the approach uses different similarity values which are based on an existing string similarity measure. The approach is independent from a specific product domain or data source.