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- W2761294890 abstract "Mobile marketing campaigns are now largely deployed through demand side platforms (DSPs) who provide dynamic customer targeting and a performance-intensive real-time bidding (RTB) version of predictive analytics as a service. Matching users with the campaigns they are most likely to engage with in extreme real-time environments requires adaptive model management, advanced parallel processing hardware/software, and the integration of multiple very large databases. The authors present (1) an adaptive modeling strategy to satisfy the performance thresholds of 40 to 100ms for DSPs to decide whether and how much to bid for a potential client to receive a particular advertisement via their mobile device. (2) a dynamic customer profiling technique to map mobile devices to specific lattices (geographic locations), and to track user behavior via device-histories. In this “big data” decision environment, analytic model management is automated via model feedback loops which adjust the models dynamically as real-time data streams in." @default.
- W2761294890 created "2017-10-20" @default.
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- W2761294890 date "2017-04-01" @default.
- W2761294890 modified "2023-09-26" @default.
- W2761294890 title "Adaptive Modeling and Dynamic Targeting for Real Time Analytics in Mobile Advertising" @default.
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- W2761294890 doi "https://doi.org/10.4018/ijssoe.2017040102" @default.
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