Two-layer recommendation-based Real Time Bidding (RTB)

摘要

Real-time bidding (RTB) has recently become the predominant technique in online advertising. Although, RTB is very effective, compared to classical approaches, a lot can be done to improve the accuracy of display advertising. In fact, a major drawback of existing RTB systems is the use of the bidding price and the current user profile as the primary features to display advertising. However, in doing so, the user may not always get the appropriate ad, and the same ad may be presented several times to the same user, which leads to its frustration. To overcome this limitation, we propose in this paper, an approach to serve the right ad to the right user at the right time. Our approach consists of incorporating the notion of recommender systems into the RTB architecture. Specifically, we design a two-layer approach. The first layer implements the item-based collaboratif filtering technique, while the second layer implements the factorization machines model. This allows to capture and use the information of other users (more specifically, those who are similar to the current user) to enhance the accuracy of display advertising. We show how these two layers collaborate to reach our goal, and validate our approach through an experimental study.