Mid-flight Forecasting for CPA Lines in Online Advertising

• URL: http://arxiv.org/abs/2107.07494v1
• Date: Thu, 15 Jul 2021 17:48:15 GMT
• Title: Mid-flight Forecasting for CPA Lines in Online Advertising
• Authors: Hao He, Tian Zhou, Lihua Ren, Niklas Karlsson, Aaron Flores
• Abstract summary: This paper investigates the forecasting problem for CPA lines in the middle of the flight. The proposed methodology generates relationships between various key performance metrics and optimization signals. The relationship between advertiser spends and effective Cost Per Action(eCPA) is also characterized.
• Score: 6.766999405722559
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a high-performance mode, but also produces actionable insights to the advertisers. In this paper, the forecasting problem for CPA lines in the middle of the flight is investigated by taking the bidding mechanism into account. The proposed methodology generates relationships between various key performance metrics and optimization signals. It can also be used to estimate the sensitivity of ad campaign performance metrics to the adjustments of optimization signal, which is important to the design of a campaign management system. The relationship between advertiser spends and effective Cost Per Action(eCPA) is also characterized, which serves as a guidance for mid-flight line adjustment to the advertisers. Several practical issues in implementation, such as downsampling of the dataset, are also discussed in the paper. At last, the forecasting results are validated against actual deliveries and demonstrates promising accuracy.

Related papers

• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. We increase the consistency and informativeness of the pairwise preference signals through targeted modifications. We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
  We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
  arXiv  Detail & Related papers  (2024-05-31T14:21:04Z)
• Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages [55.04219793298687]
  This paper shows how efficiently-solvable fair ranking models can be integrated into the training loop of Learning to Rank. In particular, this paper is the first to show how to backpropagate through constrained optimizations of OWA objectives, enabling their use in integrated prediction and decision models.
  arXiv  Detail & Related papers  (2024-02-07T20:53:53Z)
• VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness [56.87603097348203]
  VeCAF uses labels and natural language annotations to perform parametric data selection for PVM finetuning. VeCAF incorporates the finetuning objective to select significant data points that effectively guide the PVM towards faster convergence. On ImageNet, VeCAF uses up to 3.3x less training batches to reach the target performance compared to full finetuning.
  arXiv  Detail & Related papers  (2024-01-15T17:28:37Z)
• Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape [1.0923877073891446]
  This paper presents a comprehensive study that employs various time-series forecasting methods to predict daily average CPC in the online advertising market. We evaluate the performance of statistical models, machine learning techniques, and deep learning approaches, including the Temporal Fusion Transformer (TFT)
  arXiv  Detail & Related papers  (2023-02-11T19:26:17Z)
• MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation [98.09845149258972]
  We introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. With only a little workload to manually annotate these samples, the distortion of the target-domain distribution can be effectively alleviated. A powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem.
  arXiv  Detail & Related papers  (2023-01-18T07:55:22Z)
• Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization [19.857681941728597]
  This paper presents a bid optimization scenario to achieve the desired cost-per-acquisition (tCPA) goals for advertisers. We build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors.
  arXiv  Detail & Related papers  (2022-12-26T07:43:26Z)
• Functional Optimization Reinforcement Learning for Real-Time Bidding [14.5826735379053]
  Real-time bidding is the new paradigm of programmatic advertising. Existing approaches are struggling to provide a satisfactory solution for bidding optimization. This paper proposes a multi-agent reinforcement learning architecture for RTB with functional optimization.
  arXiv  Detail & Related papers  (2022-06-25T06:12:17Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• A Unified Framework for Campaign Performance Forecasting in Online Display Advertising [9.005665883444902]
  Interpretable and accurate results could enable advertisers to manage and optimize their campaign criteria. New framework reproduces campaign performance on historical logs under various bidding types with a unified replay algorithm. Method captures mixture calibration patterns among related forecast indicators to map the estimated results to the true ones.
  arXiv  Detail & Related papers  (2022-02-24T03:04:29Z)
• Bid Optimization using Maximum Entropy Reinforcement Learning [0.3149883354098941]
  This paper focuses on optimizing a single advertiser's bidding strategy using reinforcement learning (RL) in real-time bidding (RTB) We first utilize a widely accepted linear bidding function to compute every impression's base price and optimize it by a mutable adjustment factor derived from the RTB auction environment. Finally, the empirical study on a public dataset demonstrates that the proposed bidding strategy has superior performance compared with the baselines.
  arXiv  Detail & Related papers  (2021-10-11T06:53:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.