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

MCMF: Multi-Constraints With Merging Features Bid Optimization in Online Display Advertising