Search

The growth of the Internet has fuelled the availability of e-commerce marketplaces and search engines must face with a huge amount of ambiguity and inconsistencies in the data. Product matching aims at disambiguating descriptions of products belonging to different websites in order to be able to recognize identical products and to merge the content from those identical items. In this work first we evaluate some similarity measures for string matching and then, we apply a complete product matching methodology to the retail market of used cars. We use a reference or master list of items and information about a wide variety of used cars offers. The resulting linkage allows energy efficiency assignment of the model identified.

Product matching aims at disambiguating descriptions of products belonging to different websites in order to be able to recognize identical elements and to merge the content from those identical items. Most approaches face this matter applying various machine learning methods to textual product descriptions. Recently some authors are including information extracted from an image associated to a textual description of a product. Modern machine learning methods, such as content based information retrieval (CBIR) or deep learning, can be applied to this type of images since they can manage very large data sets for finding hidden structure within them, and for making accurate predictions. This information could boost the performance of the traditional textual matching but at the same time increase the computational complexity of the process. In this paper we review some CBIR and deep learning models and analyse the performance of these approaches when they are applied to images for product matching. The results obtained will help to introduce a combined classifier using textual and image information.