  In the Real-Time Bidding (RTB), advertisers are increasingly relying on bid
optimization to gain more conversions (i.e trade or arrival). Currently, the
efficiency of bid optimization is still challenged by the (1) sparse feedback,
(2) the budget management separated from the optimization, and (3) absence of
bidding environment modeling. The conversion feedback is delayed and sparse,
yet most methods rely on dense input (impression or click). Furthermore, most
approaches are implemented in two stages: optimum formulation and budget
management, but the separation always degrades performance. Meanwhile, absence
of bidding environment modeling, model-free controllers are commonly utilized,
which perform poorly on sparse feedback and lead to control instability. We
address these challenges and provide the Multi-Constraints with Merging
Features (MCMF) framework. It collects various bidding statuses as merging
features to promise performance on the sparse and delayed feedback. A cost
function is formulated as dynamic optimum solution with budget management, the
optimization and budget management are not separated. According to the cost
function, the approximated gradients based on the Hebbian Learning Rule are
capable of updating the MCMF, even without modeling of the bidding environment.
Our technique performs the best in the open dataset and provides stable budget
management even in extreme sparsity. The MCMF is applied in our real RTB
production and we get 2.69% more conversions with 2.46% fewer expenditures.
