Abstract
Online auction sites give sellers extreme high degree of freedom to fill in the product information so that they can promote their products to attract bidders in many ways. One of the most popular ways to promote is to add brand names and model names in their product titles. However, the side effect of this promotion way is that the search results are seriously irrelevant to what users expect, especially when brand names are used as query terms. In this paper, we target at the problem of retrieving the brand name of a product from its title. First, the root causes could be categorized into three types by observing the real data on the online auction site of Yahoo! Taiwan. Then, a brand-retrieving framework BRF is proposed. Specifically, BRF first eliminates those brand and model names, which may not be the actual brand name of this product, in a product title; then BRF represents a product title by selecting representative keywords with their importance; finally, BRF models the problem as a classification problem which identify what the brand name (class) of a product title is. Extensive experiments are then conducted by using real datasets, and the experimental results showed the effectiveness of BRF. To best of our knowledge, this is the first paper to design a mechanism of retrieving the brand names of products in auction sites.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Hung, C.-C., Peng, W.-C.: A regression-based approach for mining user movement patterns from random sample data. Data Knowledge Engineering 70(1), 1–20 (2011)
Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: IEEE 7th International Conference on Tools with Artificial Intelligence, pp. 338–391 (1995)
Baye, M.R., De los Santos, B., Wildenbeest, M.R.: The evolution of product search. Journal of Law, Economics & Policy 9(2), 201–221 (2012)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons (1998)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hoi, I.HI., Liao, M.L., Hung, CC., Tseng, E. (2013). BRF: A Framework of Retrieving Brand Names of Products in Auction Sites. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-39878-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39877-3
Online ISBN: 978-3-642-39878-0
eBook Packages: Computer ScienceComputer Science (R0)