Guiding creative design in online advertising

Abstract

Ad creatives (text and images) for a brand play an infuential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in fnalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the effcacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.