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Deep Landscape Forecasting in Multi-Slot Real-Time Bidding

Published: 04 August 2023 Publication History

Abstract

Real-Time Bidding (RTB) has shown remarkable success in display advertising and has been employed in other advertising scenarios, e.g., sponsored search advertising with multiple ad slots. Many current RTB techniques built for single-slot display advertising are thus no longer applicable, especially in the bid landscape forecasting. Landscape forecasting predicts market competition, including the highest bid price and winning probability, which is preliminary and crucial for the subsequent bidding strategy design. In the multi-slot advertising, predicting the winning prices for each position requires a more precise differentiation of bids among top advertisers. Furthermore, defining the winning probability and addressing censorship issues are not as straightforward as in the case of a single slot. In view of these challenges, how to forecast the bidding landscape in the multi-slot environment remains open.
In this work, we are the first to study the landscape forecasting problem in multi-slot RTB, considering the correlation between ad slots in the same pageview. Specifically, we formulate the research topic into two subproblems: predicting the distribution of the winning price and predicting the winning probability of the bid price for each position. Based on the observation from the production data and survival analysis techniques, we propose a deep recurrent model to predict the distribution of the winning price as well as the winning probability for each position. A comprehensive loss function is proposed to learn from the censoring data. Experiments on two public semi-synthetic datasets and one private industrial dataset demonstrate the effectiveness of our method.

Supplementary Material

MP4 File (adfp511-2min-promo.mp4)
Real-Time Bidding (RTB) has expanded to multi-slot scenarios, and we are the first to study the auction landscape forecasting in multi-slot RTB. This video provides a brief introduction of our main contributions.

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  • (2025)Adapting Constrained Markov Decision Process for OCPC Bidding with Delayed ConversionsACM Transactions on Information Systems10.1145/370642043:2(1-29)Online publication date: 18-Jan-2025
  • (2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
  • (2024)Robust Auto-Bidding Strategies for Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671729(1804-1815)Online publication date: 25-Aug-2024
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cover image ACM Conferences
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 August 2023

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Author Tags

  1. landscape forecasting
  2. multi-slot advertising
  3. real-time bidding
  4. survival analysis

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2025)Adapting Constrained Markov Decision Process for OCPC Bidding with Delayed ConversionsACM Transactions on Information Systems10.1145/370642043:2(1-29)Online publication date: 18-Jan-2025
  • (2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
  • (2024)Robust Auto-Bidding Strategies for Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671729(1804-1815)Online publication date: 25-Aug-2024
  • (2024)Generative Auto-bidding via Conditional Diffusion ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671526(5038-5049)Online publication date: 25-Aug-2024
  • (2024)ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645467(3497-3508)Online publication date: 13-May-2024
  • (2023)A Survey on Bid Optimization in Real-Time Bidding Display AdvertisingACM Transactions on Knowledge Discovery from Data10.1145/362860318:3(1-31)Online publication date: 9-Dec-2023

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