Preference Mapping for Automated Recommendation of Product Attributes for Designing Marketing Content

Identification of relevant product attributes is critical to the success of any marketing campaign. This task can be conceptualized 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 features 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 preferences 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 appropriateness 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.