Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes
This work addresses a specific challenge in computational advertising for advertisers, but it appears incremental as it builds on existing methods for bid optimization.
The paper tackles the problem of recommending optimal bids for advertisers to maximize return on investment under budget constraints by identifying concavity changes in click prediction curves, resulting in performance gains such as revenue and click increases in real-world advertising scenarios.
In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).