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iPinYou Global RTB Bidding Algorithm Competition Dataset

Published: 24 August 2014 Publication History

Abstract

RTB (Real Time Bidding) is one of the most exciting developments in computational advertising in recent years. It drives transparency and efficiency in the display advertising ecosystem and facilitates the healthy growth of the display advertising industry. It enables advertisers to deliver the right message to the right person at the right time, publishers to better monetize their content by leveraging their website audience, and consumers to view relevant information through personalized ads. However, researchers in computational advertising area have been suffering from lack of publicly available datasets. iPinYou organizes a three-season global RTB algorithm competition in 2013. For each season, there is offline stage and online stage. On the offline stage, iPinYou releases a dataset for model training and reserves a dataset for testing. The dataset includes logs of ad biddings, impressions, clicks, and final conversions. After the whole competition ends, iPinYou organizes and releases all these three datasets for public use. These datasets can support experiments of some important research problems such as bid optimization and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. In this paper, we give descriptions of these datasets to further boost the interests of computational advertising research community using this dataset.

Reference

[1]
Weinan Zhang, Shuai Yuan, Jun Wang, and Xuehua Shen. Real-time bidding benchmarking with ipinyou dataset. Technical report, UCL, 2014.

Cited By

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  • (2024)Efficient Bid Optimization Method for Budget Constraint Bidding in Online Advertising2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662521(3697-3702)Online publication date: 28-Jul-2024
  • (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
  • Show More Cited By

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Published In

cover image ACM Conferences
ADKDD'14: Proceedings of the Eighth International Workshop on Data Mining for Online Advertising
August 2014
65 pages
ISBN:9781450329996
DOI:10.1145/2648584
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2014

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Author Tags

  1. Demand-Side Platform(DSP)
  2. Display Advertising
  3. Real-Time Bidding(RTB)

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KDD '14
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Overall Acceptance Rate 12 of 21 submissions, 57%

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Cited By

View all
  • (2024)Efficient Bid Optimization Method for Budget Constraint Bidding in Online Advertising2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662521(3697-3702)Online publication date: 28-Jul-2024
  • (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)A Taxation Perspective for Fair Re-rankingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657766(1494-1503)Online publication date: 10-Jul-2024
  • (2024)Cost-Effective Active Learning for Bid Exploration in Online AdvertisingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635839(788-796)Online publication date: 4-Mar-2024
  • (2024)Bid Landscape Forecasting and Cold Start Problem With TransformersIEEE Access10.1109/ACCESS.2024.336049312(19117-19127)Online publication date: 2024
  • (2024)Optimizing Real-Time Bidding Strategies: An Experimental Analysis of Reinforcement Learning and Machine Learning TechniquesProcedia Computer Science10.1016/j.procs.2024.04.191235(2017-2026)Online publication date: 2024
  • (2024)Keyword-Level Bayesian Online Bid Optimization for Sponsored Search AdvertisingOperations Research Forum10.1007/s43069-024-00322-y5:2Online publication date: 22-May-2024
  • (2023)Thompson sampling with diffusion generative priorProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618954(13434-13468)Online publication date: 23-Jul-2023
  • (2023)Coordinated dynamic bidding in repeated second-price auctions with budgetsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618607(5052-5086)Online publication date: 23-Jul-2023
  • Show More Cited By

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