期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2015
卷号：28
期号：4
页码：172-175
DOI：10.14445/22312803/IJCTT-V28P132
出版社：Seventh Sense Research Group
摘要：Online Commercial data integration plays a vital role in categorizing the products from multiple providers all over the globe. An unique taxonomy is maintained by the Commercial portals and products of the providers are associated with their own taxonomy. In the existing work, an efficient and scalable approach to Catalog Integration is used which is based on the use of Source Category and Taxonomy structure Information. We formulate this intuition as a structured prediction optimization problem. Learning algorithms can actively query the user for labels. Active learning concept is used to identify candidate products for labeling and also used to obtain the desired outputs at new data points. It intends to develop the catalog integration process in automated fashion in an agent based environment in which agent can cooperate interact with the consumers to find the best classification based upon the consumer preferences.
