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- W4313303761 abstract "In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity one that can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired tCPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup." @default.
- W4313303761 created "2023-01-06" @default.
- W4313303761 creator A5017320345 @default.
- W4313303761 creator A5046957909 @default.
- W4313303761 creator A5057537474 @default.
- W4313303761 date "2022-12-26" @default.
- W4313303761 modified "2023-09-27" @default.
- W4313303761 title "Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization" @default.
- W4313303761 doi "https://doi.org/10.48550/arxiv.2212.13915" @default.
- W4313303761 hasPublicationYear "2022" @default.
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