Automated Extraction of Product Comparison Matrices From Informal Product Descriptions

Abstract : Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.
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Journal of Systems and Software, Elsevier, 2017, 124, pp.82 - 103. 〈10.1016/j.jss.2016.11.018〉
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https://hal.inria.fr/hal-01427218
Contributeur : Mathieu Acher <>
Soumis le : jeudi 5 janvier 2017 - 13:48:01
Dernière modification le : mercredi 29 novembre 2017 - 15:41:21

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Sana Ben Nasr, Guillaume Bécan, Mathieu Acher, João Ferreira Filho Bosco, Nicolas Sannier, et al.. Automated Extraction of Product Comparison Matrices From Informal Product Descriptions. Journal of Systems and Software, Elsevier, 2017, 124, pp.82 - 103. 〈10.1016/j.jss.2016.11.018〉. 〈hal-01427218〉

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