Score Look Alike Audiences
Look alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state of the art look alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real world, large scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look alike models over methods which only use pairwise similarities of users.
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)