Preference Mapping for Automated Recommendation of Product Attributes for Designing Marketing Content
Moumita Sinha, Rishiraj Saha Roy · 2014
Identification of relevant product attributes is critical to the success of any marketing campaign. This task can be con-ceptualized as an attribute recommendation problem based on the product’s content or features, where the goal of a solution would be to automatically recommend relevant fea-tures to the marketer for highlighting in a campaign. In this research, we try to solve this problem by using preference mapping, a powerful technique for associating feature pref-erences with users. We perform preference mapping with sentiment scores associated with product attributes mined from user reviews on the Web. As a result of this process, we are able to visualize a set of compared products and the ap-propriateness of the attributes on the same two-dimensional space, enabling us to easily recommend important features to a marketer. Finally, we show that expert recommendations or ratings for product features do not necessarily correlate with preference maps based on user sentiments. Categories and Subject Descriptors Information retrieval [Retrieval tasks and goals]: Rec-ommender systems