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Created page with "== Title == Concise Bid Optimization Strategies with Multiple Budget Constraints == Speaker == Google Research, New York == Abstract == A major challenge faced by the market..."
== Title ==
Concise Bid Optimization Strategies with Multiple Budget Constraints
== Speaker ==
Google Research, New York
== Abstract ==
A major challenge faced by the marketers attempting to optimize their advertising campaigns is to deal with budget constraints. The problem is even harder in the face of multidimensional budget constraints, particularly in the presence of many decision variables involved, and an interplay among the variables and decisions. Concise bidding strategies help advertisers deal with this challenge by introducing fewer variables to act on.
In this paper, we study the problem of finding optimal concise bidding strategies for advertising campaigns with multiple budget constraints. Given bid landscapes---i.e., predicted value (e.g., number of clicks) and cost per click for any bid---that are typically provided by ad-serving systems, we optimize the value given budget constraints. In particular, we consider bidding strategies that consists of no more than $k$ different bids for all keywords. For constant $k$, we provide a PTAS to optimize the profit, whereas for arbitrary $k$ we show how constant-factor approximation can be obtained via a combination of solution enumeration and dependent LP-rounding techniques.
Concise Bid Optimization Strategies with Multiple Budget Constraints
== Speaker ==
Google Research, New York
== Abstract ==
A major challenge faced by the marketers attempting to optimize their advertising campaigns is to deal with budget constraints. The problem is even harder in the face of multidimensional budget constraints, particularly in the presence of many decision variables involved, and an interplay among the variables and decisions. Concise bidding strategies help advertisers deal with this challenge by introducing fewer variables to act on.
In this paper, we study the problem of finding optimal concise bidding strategies for advertising campaigns with multiple budget constraints. Given bid landscapes---i.e., predicted value (e.g., number of clicks) and cost per click for any bid---that are typically provided by ad-serving systems, we optimize the value given budget constraints. In particular, we consider bidding strategies that consists of no more than $k$ different bids for all keywords. For constant $k$, we provide a PTAS to optimize the profit, whereas for arbitrary $k$ we show how constant-factor approximation can be obtained via a combination of solution enumeration and dependent LP-rounding techniques.
