Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach

• URL: http://arxiv.org/abs/2111.08840v1
• Date: Tue, 16 Nov 2021 23:55:02 GMT
• Title: Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach
• Authors: Max W\"urfel, Qiwei Han, Maximilian Kaiser
• Abstract summary: We propose a novel attention-based architecture to predict publishers' advertising revenues. Our results outperform several benchmark deep-learning time-series forecast models over multiple time horizons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers' advertising revenues. We leverage multiple covariates, including not only the publisher's own characteristics but also other publishers' advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.

Related papers

• Long-Term Ad Memorability: Understanding & Generating Memorable Ads [54.23854539909078]
  There has been no large-scale study on the memorability of ads. We release the first memorability dataset, LAMBDA, consisting of 1749 participants and 2205 ads covering 276 brands.
  arXiv  Detail & Related papers  (2023-09-01T10:27:04Z)
• 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)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Multilingual Disinformation Detection for Digital Advertising [0.9684919127633844]
  We make the first step towards quickly detecting and red-flaging websites that potentially manipulate the public with disinformation. We build a machine learning model based on multilingual text embeddings that first determines whether the page mentions a topic of interest, then estimates the likelihood of the content being malicious. Our system empowers internal teams to proactively blacklist unsafe content, thus protecting the reputation of the advertisement provider.
  arXiv  Detail & Related papers  (2022-07-04T10:29:20Z)
• Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
  A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
  arXiv  Detail & Related papers  (2022-01-31T11:09:59Z)
• 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)
• Learning to Infer User Hidden States for Online Sequential Advertising [52.169666997331724]
  We propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states)
  arXiv  Detail & Related papers  (2020-09-03T05:12:26Z)
• Predicting conversions in display advertising based on URL embeddings [16.63178490961762]
  We introduce and examine different models for estimating the probability of a user converting, given their history of visited URLs. Inspired by natural language processing, we introduce three URL embedding models to compute semantically meaningful URL representations.
  arXiv  Detail & Related papers  (2020-08-27T09:14:28Z)
• A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction [41.000912016821246]
  We propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. Our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment.
  arXiv  Detail & Related papers  (2020-08-20T15:08:50Z)
• 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)
• Real-Time Optimization Of Web Publisher RTB Revenues [10.908037452134302]
  This paper describes an engine to optimize web publisher revenues from second-price auctions. The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond.
  arXiv  Detail & Related papers  (2020-06-12T11:14:56Z)

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.