公开(公告)号：US20140172652A1

公开(公告)日：2014-06-19

申请号：US13720703

申请日：2012-12-19

Applicant: YAHOO! INC.

Inventor： Nageswara R. Pobbathi ,  Anlei Dong ,  Yi Chang

IPC: G06Q10/08

CPC classification number: G06Q10/087

Abstract: A system and method is described for large-scale, automated classification of products. The system and method receives information about products, wherein such information includes one or more text metadata fields associated with each product, receives a set of categories, and automatically selects one or more categories from the set of categories to which each product belongs based upon at least one of the one or more text metadata fields associated with each product. A machine learning classifier may be used to automatically select the one or more categories to which each product belongs by operating upon a feature vector for each product derived from text metadata fields of the product description. The machine learning classifier may be trained using a set of pre-categorized product descriptions. The product-category associations generated by the system and method can be used to improve search engine results or product recommendations to consumers.

Abstract translation: 描述了用于产品的大规模自动分类的系统和方法。 系统和方法接收关于产品的信息，其中这样的信息包括与每个产品相关联的一个或多个文本元数据字段，接收一组类别，并且基于每个产品所属的一组类别自动选择一个或多个类别 与每个产品相关联的一个或多个文本元数据字段中的至少一个。 机器学习分类器可以用于通过对从产品描述的文本元数据字段导出的每个产品的特征向量进行操作来自动选择每个产品所属的一个或多个类别。 可以使用一组预先分类的产品描述来训练机器学习分类器。 系统和方法生成的产品类别关联可用于将消费者的搜索引擎结果或产品建议改进。