Family Structure Attribute Estimation Method for Product Recommendation System
2017
This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.
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