In this paper, Accenture describes their initial work towards a recommender system that is capable of inferring attributes enabling them to enhance product databases. The system learns these attributes from the marketing language associated with a product by applying text learning techniques to the product descriptions found on retailer web sites. These descriptions are written by marketers to position the product in the consumer's mind in a manner that implicitly suggests these softer attributes. This paper presents a case study of a system that recommends items based on a custom-built knowledge base that consists of products and associated semantic attributes.
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