Mining the Web to Add Semantics to Retail Data Mining
While research on the Semantic Web has mostly focused on basic technologies that are needed to make the Semantic Web a reality, there has not been a lot of work aimed at showing the effectiveness and impact of the Semantic Web on business problems. This paper presents a case study where Web and Text mining techniques were used to add semantics to data that is stored in transactional databases of retailers. In many domains, semantic information is implicitly available and can be extracted automatically to improve data mining systems. This is a case study of a system that is trained to extract semantic features for apparel products and populate a knowledge base with these products and features. We show that semantic features of these items can be successfully extracted by applying text learning techniques to the descriptions obtained from websites of retailers. We also describe several applications of such a knowledge base of product semantics that we have built including recommender systems and competitive intelligence tools and provide evidence that our approach can successfully build a knowledge base with accurate facts which can then be used to create profiles of individual customers, groups of customers, or entire retail stores.
KeywordsAssociation Rule Recommender System Semantic Feature Unlabeled Data Collaborative Filter
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