Stochastic Modeling of Online Advertising System

Divsalar, Mohammad Reza | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52739 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nobakhti, Amin; Babazadeh, Maryam
  7. Abstract:
  8. Television, radio, newspaper, magazines, and billboards are among the major channels that traditionally place ads, however, the advancement of the Internet enables users to seek information online. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. Real-time means the auction is per impression and the process usually occurs less than 300 milliseconds before the ad is placed. RTB has fundamentally changed the landscape of the digital media market by scaling the buying process across a large number of available inventories among publishers in an automatic fashion.This thesis is focused on stochastic modeling of online advertising system. In this work, first all parameters in an example online advertising campaign are introduced. It is shown that the total available impressions, the highest bidding price and conversion rate are the necessary parameters for plant modeling. Then, each of them are estimated. For total available impression, a daily time pattern is fitted.A novel Approach based on particle filter for estimating the highest bidding price is developed and the two main approaches for estimating conversion rate is reviewed.then a data set is simulated. the estimation approaches are experimented on this data set. At the end, these estimating methods are used for online modeling of input-output relationship
  9. Keywords:
  10. Stochastic Modeling ; Particle Filter ; Bayesian Estimation ; Virtual Advertisements ; Online Advertising

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