This page is related to the RecSys 2019 paper 'Guiding Creative Design in Online Advertising' authored by Shaunak Mishra, Manisha Verma and Jelena Gligorijevic. The abstract of the paper, and data set are described below.

Abstract: Ad creatives (text and images) for a brand play an influential 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 finalizing 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 efficacy of the proposed model 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 strategists in a scalable manner.

Data set: We use the public data set in http://people.cs.pitt.edu/~kovashka/ads/ to extract (ground truth) keywords for brands. The authors can be emailed for further details about the data set used in the paper.