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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- text-classification |
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tags: |
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- click |
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- advertising |
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- commerce |
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size_categories: |
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- n>1T |
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--- |
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# 📊 Criteo 1TB Click Logs Dataset |
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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: |
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🔗 [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**. |
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### 🏗️ Dataset Construction |
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- The training data spans **24 days** of Criteo traffic. |
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- Each row represents a **display ad** served by Criteo. |
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- The **first column** indicates whether the ad was **clicked (1)** or **not clicked (0)**. |
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- Both **positive (clicked)** and **negative (non-clicked)** examples have been **subsampled**, though at **different rates** to keep business confidentiality. |
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## 🧱 Features |
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- **13 integer features** |
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Mostly count-based; represent numerical properties of the ad, user, or context. |
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- **26 categorical features** |
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Values are **hashed into 32-bit integers** for anonymization. |
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The **semantic meaning** of these features is **undisclosed**. |
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> ⚠️ Some features may contain **missing values**. |
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## 🧾 Data Format |
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- Rows are **chronologically ordered** |
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- Columns are **tab-separated** and follow this schema: |
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> `<label> <int_feature_1> ... <int_feature_13> <cat_feature_1> ... <cat_feature_26>` |
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- If a value is missing, the field is simply **left empty**. |
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## 🔄 Differences from Kaggle Challenge Dataset |
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- 📅 The data covers a **different time period** |
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- 🔄 **Subsampling ratios** differ |
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- 🔢 **Ordering of features** is different |
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- 🧮 Some features have **different computation methods** |
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- 🔐 **Hash function** for categorical features has changed |
