A Framework to Formulate Customer Taste from Unstructured Review Data
Bhaskarjyoti Das, V R Prathima · Procedia Computer Science · 2016
In the online marketplace, taste brings together customers and businesses. While a business can be viewed as selling products that implement specific tastes, the buying decisions of the customers are also driven by tastes. This paper attempts to model users by formulating customer's taste. A part of the taste is explicit in the online review portal's data but the foot print of taste left behind in unstructured text reviews is implicit. While the explicit part is relatively easy to understand, formulating implicit taste is challenging due to the unstructured nature of the text reviews. In the approach adopted by our work, formulating implicit taste is treated as both an information retrieval and annotation problem. The proposed framework addresses the blind spot in the current techniques of content based recommendation that works well for businesses selling products such as televisions or personal computers but does not work well for business domain such as restaurant with no clearly defined feature set. This framework promises to bring more precision to the current mechanism for marketing, recommendation and community building in such domains. This paper describes the framework and explains a specific use case such as recommendation system deriving value out of it.