An Approach for Extracting and Predicting Instance–specific Attribute Values from E–commerce Sitesfor Used Products
To obtain valuable products, reviews, rating, and other important product information given by sellers are important issues for consumers for their purchasing decisions. However, in the used–products purchasing scenario via e-commerce sites, consumers may consider much more attributes about the products than that for purchasing new products. This is due to the need for understanding instance-specific conditions before optimize their purchasing decision. Thus, the available descriptions for a used product may differ in each other, and it may drop some important information to make a decision for consumers. In this paper, we proposed a design and implementation of a system that supports users to investigate instance-specific attribute values by extracting and predicting attributes and values of used items that are selling on e-commerce sites. Our key idea is preparing a system to identify instance-specific attributes as well as their values from the descriptions of items while browsing the e-commerce sites. Also we have implemented floating style user interface to present the extracted and predicted instance-specific attributes. Our system also applies machine learning techniques to predict missing attributes values.
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Used Products details on, http://www.amazon.com/gp/offerlisting/B00VHSXBUA/ref=dp_olp_used?ie=UTF8&condition=used.
Extracted products description data on, http://www.amazon.com/gp/offer–listing/B00VHSXBUA/ref=dp_olp_used?ie=UTF8&condition=used.
J. Ben Schafer, Joseph A. Konstan and John Riedl, E–Commerce Recommendation Applications, GroupLens Research Project Department of Computer Science and Engineering University of Minnesota Minneapolis, 2001–MN 55455 1–612–625–4002, pp.01–24.
Extracted Product items information on, https://www.amazon.com/gp/offerlisting/B00F3J4B5S/ref=olp_twister_child_ie=UTF8&mv_color_name=2&mv_size_name=0&qid=1500971645&sr=1-2.