Unbiased Lift-based Bidding System

• URL: http://arxiv.org/abs/2007.04002v2
• Date: Thu, 9 Jul 2020 02:09:17 GMT
• Title: Unbiased Lift-based Bidding System
• Authors: Daisuke Moriwaki and Yuta Hayakawa and Isshu Munemasa and Yuta Saito and Akira Matsui
• Abstract summary: We develop Unbiased Lift-based Bidding System, which maximizes the advertisers' profit by accurately predicting the lift-effect from biased log data. Our system is the first to enable high-performing lift-based bidding strategy by theoretically alleviating the inherent bias in the log.
• Score: 8.959554081006285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing these easily observable metrics, however, fails to optimize the profitability of the advertisers. Rather, the bidding strategy that leads to the maximum revenue is a strategy pursuing the performance lift of showing ads to a specific user. Therefore, it is essential to predict the lift-effect of showing ads to each user on their target variables from observed log data. However, there is a difficulty in predicting the lift-effect, as the training data gathered by a past bidding strategy may have a strong bias towards the winning impressions. In this study, we develop Unbiased Lift-based Bidding System, which maximizes the advertisers' profit by accurately predicting the lift-effect from biased log data. Our system is the first to enable high-performing lift-based bidding strategy by theoretically alleviating the inherent bias in the log. Real-world, large-scale A/B testing successfully demonstrates the superiority and practicability of the proposed system.

Related papers

• FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning [48.38344934125999]
  We study how to implement high-quality data augmentation to improve recommendation fairness. Specifically, we propose FairDgcl, a dynamic graph adversarial contrastive learning framework. We show that FairDgcl can simultaneously generate enhanced representations that possess both fairness and accuracy.
  arXiv  Detail & Related papers  (2024-10-23T04:43:03Z)
• Uncertainty-Aware Instance Reweighting for Off-Policy Learning [63.31923483172859]
  We propose a Uncertainty-aware Inverse Propensity Score estimator (UIPS) for improved off-policy learning. Experiment results on synthetic and three real-world recommendation datasets demonstrate the advantageous sample efficiency of the proposed UIPS estimator.
  arXiv  Detail & Related papers  (2023-03-11T11:42:26Z)
• Real-time Bidding Strategy in Display Advertising: An Empirical Analysis [0.20999222360659603]
  This paper describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. We quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset.
  arXiv  Detail & Related papers  (2022-11-30T05:50:43Z)
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning [168.89470249446023]
  We present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the preference-based method on a variety of locomotion and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-18T16:50:38Z)
• 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)
• A Real-World Implementation of Unbiased Lift-based Bidding System [8.35120274877627]
  A typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding. Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves target metrics such as sales.
  arXiv  Detail & Related papers  (2022-02-23T01:15:54Z)
• 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)
• We Know What You Want: An Advertising Strategy Recommender System for Online Advertising [26.261736843187045]
  We propose a recommender system for dynamic bidding strategy recommendation on display advertising platform. We use a neural network as the agent to predict the advertisers' demands based on their profile and historical adoption behaviors. Online evaluations show that the system can optimize the advertisers' advertising performance.
  arXiv  Detail & Related papers  (2021-05-25T17:06:59Z)
• Enhancing User' s Income Estimation with Super-App Alternative Data [59.60094442546867]
  It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators. Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.
  arXiv  Detail & Related papers  (2021-04-12T21:34:44Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)

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.