# Real-Time Bidding Benchmarking with iPinYou Dataset

arXiv: https://arxiv.org/abs/1407.7073 · HTML: https://mlsanity.com/paper/1407.7073/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2014.

## Built on by (most cited first, 19 of 19)

- [Online Ad Allocation with Predictions](https://mlsanity.com/paper/2302.01827/) (2023)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets](https://mlsanity.com/paper/2306.07709/) (2023)
- [Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising](https://mlsanity.com/paper/2110.13587/) (2021)
- [xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions](https://mlsanity.com/paper/2301.01089/) (2023)
- [Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness](https://mlsanity.com/paper/2605.12840/) (2026)
- [AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.08353/) (2023)
- [Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions](https://mlsanity.com/paper/2605.09448/) (2026)
- [PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective](https://mlsanity.com/paper/2607.27265/) (2026)
- [Risk-Aware Bid Optimization for Online Display Advertisement](https://mlsanity.com/paper/2210.15837/) (2022)
- [DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction](https://mlsanity.com/paper/2301.08139/) (2023)
- [Improving Real-Time Bidding in Online Advertising Using Markov Decision Processes and Machine Learning Techniques](https://mlsanity.com/paper/2305.04889/) (2023)
- [Online and Streaming Algorithms for Constrained k-Submodular Maximization](https://mlsanity.com/paper/2305.16013/) (2023)
- [Feature Staleness Aware Incremental Learning for CTR Prediction](https://mlsanity.com/paper/2505.02844/) (2025)
- [DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation](https://mlsanity.com/paper/2506.21624/) (2025)
- [Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion](https://mlsanity.com/paper/2601.20422/) (2026)
- [Knowledge-informed Bidding with Dual-process Control for Online Advertising](https://mlsanity.com/paper/2603.04920/) (2026)
- [Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis](https://mlsanity.com/paper/2607.04315/) (2026)
- [When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions](https://mlsanity.com/paper/2609.33312/) (2026)

## Abstract (arXiv)

Being an emerging paradigm for display advertising, Real-Time Bidding (RTB) drives the focus of the bidding strategy from context to users' interest by computing a bid for each impression in real time. The data mining work and particularly the bidding strategy development becomes crucial in this performance-driven business. However, researchers in computational advertising area have been suffering from lack of publicly available benchmark datasets, which are essential to compare different algorithms and systems. Fortunately, a leading Chinese advertising technology company iPinYou decided to release the dataset used in its global RTB algorithm competition in 2013. The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users' responses from advertisers' perspective. This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. Thus, they are valuable for reproducible research and understanding the whole RTB ecosystem. In this paper, we first provide the detailed statistical analysis of this dataset. Then we introduce the research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol. Besides, a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid optimisation.