Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/29779
metadata.artigo.dc.title: An association rules based method for classifying product offers from e-shopping
metadata.artigo.dc.creator: Oliveira, Claudiane Maria
Pereira, Denilson Alves
metadata.artigo.dc.subject: Association rule
Entity resolution
E-commerce
Regra de associação
Resolução de entidades
Comércio eletrônico
metadata.artigo.dc.publisher: IOS Press
metadata.artigo.dc.date.issued: 2017
metadata.artigo.dc.identifier.citation: OLIVEIRA. C. M.; PEREIRA, D. A. An association rules based method for classifying product offers from e-shopping. Intelligent Data Analysis, [S. l.], v. 21, n. 3, p. 637-660, 2017.
metadata.artigo.dc.description.abstract: Price comparison services are widely used by e-shopping customers. Such e-shopping sites receive product offers from thousands of online stores, and in order to provide price comparison, product categorization, and searching, it is necessary to match different offers referring to the same real-world product. This is a hard task, since they need to classify millions of product offers in thousands of classes, and distinct descriptions may exist for the same product, as well as very similar descriptions of distinct products. In this work, we propose a method that uses association rules to classify product offers from e-shopping web sites matching offers against offers without the need for a product catalog. This is a supervised learning method that trains a classifier, whose generated model comprises a set of association rules to identify product offer classes. Experimental evaluations show that our method is effective and efficient, and obtains better results than three baselines in several datasets with distinct characteristics. It is able to deal with large datasets containing thousands of classes and different types of products such as electronics and books. Moreover, we propose and evaluate strategies to reduce its execution time and we evaluate its weaknesses.
metadata.artigo.dc.identifier.uri: https://content.iospress.com/articles/intelligent-data-analysis/ida150444
http://repositorio.ufla.br/jspui/handle/1/29779
metadata.artigo.dc.language: en_US
Appears in Collections:DCC - Artigos publicados em periódicos

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