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Machine Learning and Applications, Fourth International Conference on (2010)
Washington, D.C., USA
Dec. 12, 2010 to Dec. 14, 2010
ISBN: 978-0-7695-4300-0
pp: 23-28
One central issue in a long-tail online marketplace such as eBay is to automatically put user self-input items into a catalog in real time. This task is extremely challenging when the inventory scales up, the items become ephemeral, and the user input remains noisy. Indeed, catalog learning has emerged as a key technical property for other major online ecommerce applications including search and recommendation. We formulate the item cataloging task as a Bayesian classification problem, which shall scale well in very large data set and have good online prediction performance. The inherent data sparseness issue, especially for those tail categories, is key to the overall model performance. We address the data sparseness issue by adapting statistically sound smoothing methods well studied in language modeling tasks. However, there are data characteristics specific to the ecommerce domain, including short yet focused item description, very large and hierarchical catalog taxonomy, and highly skewed distribution over types of items. We investigate these domain-specific regularities empirically, and report practically significant results with real-world true-scale data.
item categorization, catalog, smoothing, hierarchy

N. Sundaresan, J. Ruvini, D. Shen and R. Mukherjee, "A Study of Smoothing Algorithms for Item Categorization on e-Commerce Sites," Machine Learning and Applications, Fourth International Conference on(ICMLA), Washington, D.C., USA, 2010, pp. 23-28.
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