| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - text-classification |
| tags: |
| - click |
| - advertising |
| - commerce |
| size_categories: |
| - n>1T |
| --- |
| |
| # 📊 Criteo 1TB Click Logs Dataset |
|
|
| This dataset contains **feature values and click feedback** for millions of display ads. Its primary purpose is to **benchmark algorithms for clickthrough rate (CTR) prediction**. |
|
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| It is similar, but larger than the dataset released for the Display Advertising Challenge hosted by Kaggle: |
| 🔗 [Kaggle Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge) |
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|
| ## 📁 Full Description |
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|
| This dataset contains **24 files**, each corresponding to **one day of data**. |
|
|
| ### 🏗️ Dataset Construction |
|
|
| - The training data spans **24 days** of Criteo traffic. |
| - Each row represents a **display ad** served by Criteo. |
| - The **first column** indicates whether the ad was **clicked (1)** or **not clicked (0)**. |
| - Both **positive (clicked)** and **negative (non-clicked)** examples have been **subsampled**, though at **different rates** to keep business confidentiality. |
|
|
| ## 🧱 Features |
|
|
| - **13 integer features** |
| Mostly count-based; represent numerical properties of the ad, user, or context. |
| |
| - **26 categorical features** |
| Values are **hashed into 32-bit integers** for anonymization. |
| The **semantic meaning** of these features is **undisclosed**. |
|
|
| > ⚠️ Some features may contain **missing values**. |
|
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|
|
| ## 🧾 Data Format |
|
|
| - Rows are **chronologically ordered** |
| - Columns are **tab-separated** and follow this schema: |
| > `<label> <int_feature_1> ... <int_feature_13> <cat_feature_1> ... <cat_feature_26>` |
| - If a value is missing, the field is simply **left empty**. |
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|
|
| ## 🔄 Differences from Kaggle Challenge Dataset |
|
|
| - 📅 The data covers a **different time period** |
| - 🔄 **Subsampling ratios** differ |
| - 🔢 **Ordering of features** is different |
| - 🧮 Some features have **different computation methods** |
| - 🔐 **Hash function** for categorical features has changed |
