@inproceedings{rivas-sanchez_using_2017,
	address = {Cadiz, Spain},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Using {Deep} {Learning} for {Image} {Similarity} in {Product} {Matching}},
	isbn = {978-3-319-59152-0 978-3-319-59153-7},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-59153-7_25},
	doi = {10.1007/978-3-319-59153-7_25},
	abstract = {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.},
	language = {en},
	urldate = {2018-05-03TZ},
	booktitle = {Advances in {Computational} {Intelligence}},
	publisher = {Springer},
	author = {Rivas-Sánchez, Mario and Guerrero-Lebrero, Maria De La Paz and Guerrero, Elisa and Bárcena-Gonzalez, Guillermo and Martel, Jaime and Galindo, Pedro L.},
	month = jun,
	year = {2017},
	keywords = {AI=Artificial Intelligence, Algorithms, CNN=Convolutional Neural Network, Classification, Computer Science, Computer Science - Computer Vision and Pattern Recognition, Computer Vision, DNN=Deep Neural Networks, Data Science, Deep Learning, Image Analysis, Informatics, Machine Learning, Machine Learning Applications, Neural Networks, Statistics - Machine Learning},
	pages = {281--290}
}

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L."],"year":2017,"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/3116/collections/5RDQSYPF/items?key=QIh5RYbGeOYPifpPi4oSlmhz&format=bibtex&limit=100","bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Cadiz, Spain","series":"Lecture Notes in Computer Science","title":"Using Deep Learning for Image Similarity in Product Matching","isbn":"978-3-319-59152-0 978-3-319-59153-7","url":"https://link.springer.com/chapter/10.1007/978-3-319-59153-7_25","doi":"10.1007/978-3-319-59153-7_25","abstract":"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. 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