Abstract
This work describes a system for analyzing e-commerce markets, by tracking the consumers’ habits in a set of webstores. In order to retrieve the types of products that are mostly purchased or viewed by a sample of consumers, we extract product titles displayed at the pages browsed by the monitored sample. Then, the collected titles are used as input to an automatic classification process that aims to assign each product to a suitable category and brand. We introduce a graph-based classifier that explores the relation between the categories and brands to be determined and is built from a supervised training set. We will show that the classifier was able to obtain good results, improving a Bayesian technique and allowing a further analysis of the considered market.
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References
Angelova, R., Weikum, G.: Graph-based text classification: learn from your neighbors. In: SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 485–492. ACM, New York (2006)
Bergamaschi, S., Guerra, F., Vincini, M.: Product classification integration for e-commerce. In: Proceedings of the 13th International Workshop on Database and Expert Systems Applications, DEXA 2002, pp. 861–867. IEEE Computer Society, Washington, DC (2002)
Bratko, A., Filipič, B.: Exploiting structural information for semi-structured document categorization. Inf. Process. Manage. 42, 679–694 (2006)
Cheema, A., Papatla, P.: Relative importance of online versus offline information for internet purchases: Product category and internet experience effects. Journal of Business Research 63(9-10), 979–985 (2010)
Kiang, M.Y., Ye, Q., Hao, Y., Chen, M., Li, Y.: A service-oriented analysis of online product classification methods. Decision Support Systems (2011)
Kim, Y.-G., Lee, T., Chun, J.H., Lee, S.-G.: Modified Naïve Bayes Classifier for E-Catalog Classification. In: Lee, J., Shim, J., Lee, S.-g., Bussler, C.J., Shim, S. (eds.) DEECS 2006. LNCS, vol. 4055, pp. 246–257. Springer, Heidelberg (2006)
Kim, Y.-G., Lee, T., Lee, S.-G., Park, J.-H.: Exploiting Attribute-Wise Distribution of Keywords and Category Dependent Attributes for E-Catalog Classification. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS, vol. 5226, pp. 985–992. Springer, Heidelberg (2008)
Korgaonkar, P., Becerra, E., O’Leary, B., Goldring, D.: Product classifications, consumer characteristics, and patronage preference for online auction. Journal of Retailing and Consumer Services (March 2010)
Omelayenko, D.K., Ding, Y., Korotkiy, M., Omelayenko, B., Kartseva, V., Zykov, V., Klein, M., Schulten, E., Fensel, D.: Goldenbullet: Automated classification of product data in e-commerce. In: Proceedings of Business Information Systems Conference BIS 2002 (2002)
Poong, Y., Zaman, K.-U., Talha, M.: E-commerce today and tomorrow: a truly generalized and active framework for the definition of electronic commerce. In: Proceedings of the 8th International Conference on Electronic Commerce: The New E-commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, ICEC 2006, pp. 553–557. ACM, New York (2006)
Volker, J.L., Schmitz, V., Dorloff, F.D.: A modeling approach for product classification systems. In: Proc. of the 13th International Workshop on Database and Expert Systems Applications (DEXA 2002), Aix-en-Provence, pp. 868–874 (2002)
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Mattos, A.B., Van Kampen, M., Carriço, C., Dias, A.R., Crivellaro, A. (2012). E-commerce Market Analysis from a Graph-Based Product Classifier. In: Caseli, H., Villavicencio, A., Teixeira, A., Perdigão, F. (eds) Computational Processing of the Portuguese Language. PROPOR 2012. Lecture Notes in Computer Science(), vol 7243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28885-2_33
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DOI: https://doi.org/10.1007/978-3-642-28885-2_33
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