NDLI: A Study of Smoothing Algorithms for Item Categorization on e-Commerce Sites

Content Provider	IEEE Xplore Digital Library
Author	Dan Shen Ruvini, J. Mukherjee, R. Sundaresan, N.
Copyright Year	2010
Abstract	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.
Starting Page	23
Ending Page	28
File Size	192925
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Maximum likelihood estimation Vocabulary item categorization Smoothing methods Bayesian methods Training data catalog hierarchy smoothing Catalogs
Content Type	Text
Resource Type	Article

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