  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).
